1 Raw data
2 Continuous Phenotype
- 2.1 Categorical Phenotype
- 2.2 Removing “outliers”
3 GENOTYPES
- 3.1 Transforming the Genotypes in 012 format to plink format
4 GWAS
5 Quality Control
6 KING
7 PCA
- 7.1 PCA Plot
8 GWAS on GCTA
9 GWAS - EXTREME PHENO - WITH BY and SD
10 Genetic Correlation
- 10.1 Data preparation
- 10.2 Correlations - Linear Regression
  - 10.2.1 Adding BIRTH_YEAR and SAMPLING DATE
11 Heritability estimation - BLUPF90

1 Raw data

data <- read.csv("/home/bambrozi/2_CORTISOL/Data/T4_Elora_Data_04_25_2024.csv")

# Replace "treated" with NA
data$T4Cortisol[data$T4Cortisol == "treated" | data$T4Cortisol == "Treated at T2" | data$T4Cortisol == "treated at T2"] <- NA
# Convert the column to numeric, coercing non-numeric values to NA
data$T4Cortisol <- as.numeric(as.character(data$T4Cortisol))
#Filtering only the lines with values
data <- data[!is.na(data$T4Cortisol),]
#creating new data file cleaned  
write.csv(data, "/home/bambrozi/2_CORTISOL/Data/data_clean.csv", row.names = F)

print(data)

2 Continuous Phenotype

# Summary Statistics
summary(data$T4Cortisol)
# Histogram
hist(data$T4Cortisol, breaks = 20, main = "Histogram of T4 Cortisol", xlab = "T4 Cortisol")
# Boxplot
boxplot(data$T4Cortisol, main = "Boxplot of T4 Cortisol", ylab = "T4 Cortisol")
# Density Plot
plot(density(data$T4Cortisol), main = "Density Plot of T4 Cortisol", xlab = "T4 Cortisol", ylab = "Density")
# Calculate the theoretical quantiles
qqnorm(data$T4Cortisol, main = "QQ Plot of T4Cortisol", xlim = c(min(qqnorm(data$T4Cortisol)$x), max(qqnorm(data$T4Cortisol)$x)), ylim = c(min(qqnorm(data$T4Cortisol)$y), max(qqnorm(data$T4Cortisol)$y) + 2 * IQR(qqnorm(data$T4Cortisol)$y)))
# Add the QQ line
qqline(data$T4Cortisol, col = "red")

Summary statistics My Image

Histogram My Image

Density My Image

Box_Plot My Image

qq_Plot My Image

Shapiro-Wilk normality test My Image

2.1 Categorical Phenotype

I received from Umesh a e-mail informing the three categories that the animals could be sorted based on their cortisol concentration.

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data$Categorical <- ifelse(data$T4Cortisol >= 956, "H", 
                           ifelse(data$T4Cortisol <= 190.8, "L", "M"))

table(data$Categorical)
library(ggplot2)

# Reorder the levels of the 'Categorical' column
data$Categorical <- factor(data$Categorical, levels = c("L", "M", "H"))

# Create the histogram with reordered categories
ggplot(data, aes(x = Categorical, fill = Categorical)) +
  geom_bar() +
  labs(title = "Histogram of T4Cortisol by Category",
       x = "Category",
       y = "Frequency") +
  theme_minimal()

# Create the histogram
ggplot(data, aes(x = T4Cortisol, fill = Categorical)) +
  geom_histogram(binwidth = 50, color = "black", alpha = 0.7) + # Adjust binwidth as needed
  labs(title = "Histogram of T4Cortisol with Color by Category",
       x = "T4 Cortisol",
       y = "Frequency",
       fill = "Category") +
  scale_fill_manual(values = c("H" = "red", "M" = "blue", "L" = "green")) + # Adjust colors if needed
  theme_minimal()

# Create the density plot
ggplot(data, aes(x = T4Cortisol, fill = Categorical)) +
  geom_density(alpha = 0.3) +
  labs(title = "Density Plot of T4Cortisol with Color by Category",
       x = "T4Cortisol",
       y = "Density",
       fill = "Category") +
  scale_fill_manual(values = c("H" = "red", "M" = "blue", "L" = "green")) + # Adjust colors if needed
  theme_minimal()

# Create a density plot
ggplot(data, aes(x = T4Cortisol)) +
  geom_density() +
  geom_vline(xintercept = c(956, 190.8), linetype = "dashed", color = "red") +
  labs(title = "Density Plot of T4Cortisol with Vertical Lines",
       x = "T4Cortisol",
       y = "Density") +
  theme_minimal()

The animals were sorted in these three categories >H = Hight >M = Medium >L = Low

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The individuals frequency distribution in theese categories are shown in the plots below

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2.2 Removing “outliers”

Observing the previous plots I tried to remove the “outliers” phenotypes above 1250, but the outcome from Shapiro test is still indicating no normality of the data.

library(tidyverse)

data_no_out <- filter(data, T4Cortisol < 1250)

# Create QQ plot
qqnorm(data_no_out$T4Cortisol, main = "QQ Plot of T4Cortisol", xlab = "Theoretical Quantiles", ylab = "Sample Quantiles")
qqline(data$T4Cortisol, col = "red")

boxplot(data_no_out$T4Cortisol, main = "Boxplot of T4 Cortisol", ylab = "T4 Cortisol")

hist(data_no_out$T4Cortisol, breaks = 20, main = "Histogram of T4 Cortisol", xlab = "T4 Cortisol")

shapiro.test(data$T4Cortisol)

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3 GENOTYPES

Lucas Alcântara sent me the path to the genotype and pedigree files: /data/cgil/daiclu/6_Data/database/public_output/bruno

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In this folder we found the following files:

bruno_gntps.txt: the file with the genotypes in 012 format
bruno_ids.csv: file with elora_id, cdn_id, iid
bruno_ped.id.csv: pedigree with cdn_id
bruno_ped.iid.csv: pedigree with iid

I made a copy of this files in a folder called Raw_files:

/home/bambrozi/2_CORTISOL/RawFiles

This directory has two sub-directories:

Genotypes
Pedigree

3.1 Transforming the Genotypes in 012 format to plink format

To perfome this I used two codes

Add “column names”

sed -i '1i id Call' genotypes.txt

Recode in python

filename = 'bruno_gntps.txt'

outputFileOpen = open('genoplink.ped','w')


recode = {'0':['C','C'] , '1':['A','C'] , '2':['A','A'] , '5':['0','0'] }
for line in open(filename,'r'):
    if 'Call' in line : continue

    ids, call = line.strip().split()
    genotypes = [ recode[geno012][0] +' '+ recode[geno012][1] for geno012 in call ]

    outputFileOpen.write("%s %s %s %s %s %s %s\n" % ('HO', ids, '0','0','0','-9',' '.join(genotypes)) )

Now I have a folder called /home/bambrozi/2_CORTISOL/Geno_files with the file named genoplink.ped

4 GWAS

4.1 Checking with Phenotyped Animals also have Genotype

library(data.table)

pheno <- read.csv("/home/bambrozi/2_CORTISOL/Data/data_clean.csv")
ped <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/Pedigree/bruno_ids.csv")
geno <- fread("/home/bambrozi/2_CORTISOL/Geno_files/genoplink.ped")
geno <- geno[,c("V2")]

#Bringing cdn_id to my phenotype file
#Generate a index with the match
matching_indices <- match(pheno$ID, ped$elora_id)
# Then, assign 'cdn_id' from 'ped' to 'pheno' where there are matches
pheno$cdn_id <- ifelse(!is.na(matching_indices), ped$cdn_id[matching_indices], NA)

#Making a phenotype file only with genotyped animals
pheno_genotyped <- pheno[pheno$cdn_id %in% geno$V2,] 

#check if all animals in this file are genotyped
checkk <- pheno_genotyped$cdn_id %in% geno$V2
sum(checkk)

4.2 Generating a Phenotype file

The phenotype file should have three columns: FID, Animal_id, Phenotype

HO <- rep("HO", 252)

pheno_gwas <- as.data.frame(cbind(HO, pheno_genotyped$cdn_id, pheno_genotyped$T4Cortisol))

colnames(pheno_gwas) <- c("FID", "cdn_id", "cortisol")

pheno_gwas$cdn_id <-  as.numeric(pheno_gwas$cdn_id)
pheno_gwas$cortisol <- round(as.numeric(pheno_gwas$cortisol),2)

write.table(pheno_gwas, "/home/bambrozi/2_CORTISOL/GWAS/pheno_genotyped.txt", quote = F, row.names = F, col.names = T)

4.3 Adjusting the SNP_map to .map

map <- fread("/data/cgil/daiclu/6_Data/database/public_output/bruno/DGVsnpinfo.2404.ho")
morgan <- data.frame(X0 = rep(0, 45101))
mapa=as.data.frame(cbind(map$chr, map$snp_name, morgan$X0, map$location))
head(mapa)
write.table(x = mapa, file = "/home/bambrozi/2_CORTISOL/Geno_files/genoplink.map", row.names = FALSE, col.names = FALSE, sep = "\t", quote = FALSE)

4.4 Generating the bfiles

system("/home/local/bin/plink --cow --nonfounders --allow-no-sex --keep-allele-order --file /home/bambrozi/2_CORTISOL/Geno_files/genoplink --make-bed --out /home/bambrozi/2_CORTISOL/Geno_files/genoplink")

With the code above I generated the bfiles:

genoplink.bim
genoplink.fam
genoplink.log
genoplink.nosex

5 Quality Control

We ran the code below to perfom the QC

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/Geno_files/genoplink
RESULT=/home/bambrozi/2_CORTISOL/Geno_files_after_QC/genoplink_clean

/home/local/bin/plink \
    --bfile ${DATA} \
    --cow \
    --allow-no-sex \
    --hwe 1e-5 \
    --maf 0.01 \
    --geno 0.1 \
    --mind 0.1 \
    --keep-allele-order \
    --make-bed \
    --out ${RESULT}

The server screen outcome is shown below. My Image

After the Quality Control we ended up with

252 samples
42,278 variants (SNPs)

6 KING

To check for duplicated individuals I performed the KINSHIP analysis using one script from Larissa Braga. Running the King Analysis on Plink.

#!/bin/bash

DATA=/home/bambrozi/Extrm_ARS1_GrassHill_1/GENOTYPES/ONLY_GRASSHILL_AND_PHENO_after_QC/only_grasshill_and_pheno_clean
RESULT=/home/bambrozi/Extrm_ARS1_GrassHill_1/GENOTYPES/KING/result_king

plink2 \
    --bfile ${DATA} \
    --chr-set 29 \
    --make-king-table \
    --out ${RESULT}

This is the output screen on terminal:

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The table below is the output /home/bambrozi/2_CORTISOL/Geno_files_after_KING/result_king.kin0 and have pairwise comparisons between all individuals.

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Now we should open in R and check for individuals with more than 0,354, to perform this we can use the code below, also provided by Larissa Braga:

setwd("/home/bambrozi/2_CORTISOL/Geno_files_after_KING")

relatedness="result_king.kin0" ## change accordingly!!

library(data.table)

print(relatedness)
rel=fread(relatedness, h = T)
head(rel)

print("Individuals with different identifications above the cut off of 0.354:")
dup=subset(rel, KINSHIP >= 0.354  & IID1!=IID2)
print(dup)
nrow(dup)

So the code above will provide this output if there is any duplicated individual.

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We do not have any duplicated individual

So the file to be used are those in the directory /home/bambrozi/2_CORTISOL/Geno_files_after_QC

files:genoplink_clean

After Quality Control we didn’t lost any animal, so we don’t need to update our phenotype file

7 PCA

Now before performing the PCA analysis we need to change the FID for those individuals that has phenotype = 1 for Nadia.

#!/bin/bash

DATA=/home/bambrozi/Extrm_ARS1_GrassHill_1/PCA/imput_pca
RESULT=/home/bambrozi/Extrm_ARS1_GrassHill_1/PCA/pca_result

plink \
    --bfile ${DATA} \
    --keep-allele-order \
    --chr-set 29 \
    --pca \
    --out ${RESULT}

The PCA output:

My Image

7.1 PCA Plot

After generate the Eigenvalues and Eigenvectors I used the code below to generate the PCA Plot

setwd("/home/bambrozi/2_CORTISOL/PCA")

library(ggplot2) 
library(tidyverse)

##
# Visualize PCA results
###

# read in result files
eigenValues <- read_delim("pca_result.eigenval", delim = " ", col_names = F)
eigenVectors <- read_delim("pca_result.eigenvec", delim = " ", col_names = F)

## Proportion of variation captured by each vector
eigen_percent <- round((eigenValues / (sum(eigenValues))*100), 2)


# PCA plot
png("pca-plink.eng.png", width=5, height=5, units="in", res=300)
ggplot(data = eigenVectors) +
  geom_point(mapping = aes(x = X3, y = X4), color = "red", shape = 19, size = 1, alpha = 1) +
  geom_hline(yintercept = 0, linetype="dotted") +
  geom_vline(xintercept = 0, linetype="dotted") +
  labs(x = paste0("Principal component 1 (", eigen_percent[1,1], " %)"),
       y = paste0("Principal component 2 (", eigen_percent[2,1], " %)")) +
  theme_minimal()
dev.off()


# PCA plot with animal ids
png("pca-plink.eng.animals_id.png", width=50, height=50, units="in", res=300)
ggplot(data = eigenVectors) +
  geom_point(mapping = aes(x = X3, y = X4), color = "red", shape = 19, size = 5, alpha = 1) +
  geom_text(mapping = aes(x = X3, y = X4, label = X2), size = 2, hjust = 0, vjust = 0) +  # Add labels for animal IDs
  geom_hline(yintercept = 0, linetype="dotted") +
  geom_vline(xintercept = 0, linetype="dotted") +
  labs(x = paste0("Principal component 1 (", eigen_percent[1,1], " %)"),
       y = paste0("Principal component 2 (", eigen_percent[2,1], " %)")) +
  theme_minimal()
dev.off()

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8 GWAS on GCTA

Previously we have performed GWAS on GCTA:

With the whole sample and no fixed effects
With the whole sample and with Birht Year as fixed effect
With the whole sample and with Birth Year and Sampling Data as fixed effects

9 GWAS - EXTREME PHENO - WITH BY and SD

9.1 Data preparation

I received from Umesh a e-mail informing the three categories that the animals could be sorted based on their cortisol concentration.

My Image

pheno <- read.table("/home/bambrozi/2_CORTISOL/GWAS/pheno_genotyped.txt", header = T)
data_final <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/data_GEBVs_Cortisol_select_traits2.csv", header = T)
ids_eq <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/Pedigree/bruno_ids.csv", header = T)


# Create an matrix with fixed effects with only those animals which also have phenotype and genotype
data_final$cdn_id <- ids_eq$cdn_id[match(data_final$ID, ids_eq$elora_id)]
fixeff <- data_final[,c("ID", "BIRTH_YEAR", "Sampling_date", "cdn_id")]
fixeff <- fixeff[fixeff$cdn_id %in% pheno$cdn_id, ]
fixeff$FID <- "HO"
fixeff <- fixeff[, c("FID", "cdn_id", "BIRTH_YEAR", "Sampling_date")]

# Now we are gona remove the intermediary animals from pheno object
pheno$Categorical <- ifelse(pheno$cortisol >= 956, "H", 
                           ifelse(pheno$cortisol <= 190.8, "L", "M"))
table(pheno$Categorical)
pheno <- pheno[pheno$Categorical != "M", ]
pheno <- pheno[, c("FID", "cdn_id", "cortisol")]

# Now we are going to remove from fixeff the animals which are not in pheno
fixeff <- fixeff[fixeff$cdn_id %in% pheno$cdn_id,]

#Checking if match the animals and order
identical(fixeff$cdn_id, pheno$cdn_id)

#Creating a file with animals to keep in the genotype file, we will use it on Plink
to_keep_geno <- pheno[, c("FID", "cdn_id")]

write.table(fixeff, "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/fixeff.txt", quote = F, row.names = F, col.names = T)
write.table(pheno, "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/pheno.txt", quote = F, row.names = F, col.names = T)
write.table(to_keep_geno, "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/to_keep_geno.txt", quote = F, row.names = F, col.names = F)

We ended up with
H (Hight) = 34 animals
L (Low) = 37 animals Total = 71 animals

On Plink we will remove all individuals from genotype files that are classified as Medium, keeping only the High and Low

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/Geno_files_after_QC/genoplink_clean
RESULT=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/geno_extreme
KEEP=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/to_keep_geno.txt

plink2 --bfile ${DATA} --keep ${KEEP} --chr-set 29 --make-bed --out ${RESULT}

9.2 GWAS on GCTA

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/geno_extreme
RESULT=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/GWAS_result
PHENO=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/pheno.txt
FIXEFF=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/fixeff.txt

/home/local/bin/gcta \
    --bfile ${DATA} \
    --mlma-loco \
    --pheno ${PHENO} \
    --qcovar ${FIXEFF} \
    --autosome-num 29 \
    --autosome \
    --out ${RESULT}

After ran the GWAS above I got the following message from the GCTA:

Error: analysis stopped because more than half of the variance components are constrained. The result would be unreliable. You may have a try of the option –reml-no-constrain.

As we got this error message, we needed to solve this problem, and for that we used the whole sample size (252 individuals) to estimate the variance components, and after this, using this variance components from the whole sample we performed the ssGWAS with the subset of individuals (34 High + 37 Low = 71), but this was not possible in GCTA so we switched to another software (BLUPF90+)

9.3 BLUPF90+ GWAS

To run ssGWAS on Blupf90+ suite, we will need 4 different softwares:

renum: just to renumerate the files and generate the renf90.par
preGSF90: just to perfome a quality control with different parameter from the default.
blupf90+: used to estimate VCE and generate Ainv and Ginv
postGSF90: perform GWAS

The tutorial for preGSF90 and postGSF90 we can find in the link bellow https://nce.ads.uga.edu/wiki/doku.php?id=readme.pregsf90#gwas_options_postgsf90

According to the BLUPF90+ tutorial:

ssGWAS is an iterative procedure with 2 steps:
0) Quality control
1) prediction of GEBV with ssGBLUP
2) prediction of SNP marker effects based on the GEBV

9.3.1 Files preparation

Preparing files to run Variance components estimation using REML with AI (Average Information) algorithm.

First you need to create a directory in your home directory, prepare and save the following files in:

Phenotype and Fixed effects file
Pedigree file
Genotype file
BlupF90+ executable file
RenumF90 executable file
preGSf90 executable file
postGSf90 executable file
Parameter file

9.3.2 Download the executable files

Download from this website https://nce.ads.uga.edu/html/projects/programs/Linux/64bit/:

BlupF90+ = we will use to estimate the Variance components and GEBVs
renumF90 = we will use to renumerate the files
preGSf90 = we will use to perform the Quality control
postGSf90 = we will use for GWAS

9.3.3 SNP MAP

mapfile <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/DGVsnpinfo.2404.ho", header = T)

colnames(mapfile)

colnames(mapfile) <- c("SNP_ID", "CHR", "POS")

mapfile <- mapfile[,c("CHR", "POS", "SNP_ID")]

write.table(mapfile, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/snpmap.txt", col.names = T, row.names = F, quote = F)

9.3.4 Parameter file for Quality Control

But, before running the GEBV we will first perform one additional step to “CLEAN” our genotypes. Actually BLUPF90 by default perform a data cleaning with pre set parameters, but as HWE is not used by default, we will perform this additional step.

The Parameter card for this step is the parameter bellow:

/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/renum_QC.par


DATAFILE
fenofix.txt
TRAITS
2
FIELDS_PASSED TO OUTPUT

WEIGHT(S)

RESIDUAL_VARIANCE
1.0
EFFECT
3 cross numer
EFFECT
4 cross alpha
EFFECT
1 cross alpha
RANDOM
animal
FILE
bruno_ped_iid_blup.txt
FILE_POS
1 2 3 0 0
SNP_FILE
bruno_gntps_iid
PED_DEPTH
0
(CO)VARIANCES
1.0
OPTION outcallrate
OPTION saveCleanSNPs
OPTION minfreq 0.01
OPTION map_file snpmap.txt
OPTION excludeCHR 30 31

DATAFILE: bellow this line you need to inform the name of the file with phenotype and fixed effects. As before running BLUPF90 on server you are going to direct the terminal to the directory where all these files are placed you only need to inform the name.
TRAITS: below this line you need to inform which column are the phenotype date in the previous file, in this example, 2.
FIELDS_PASSED TO OUTPUT:
WEIGHT (S):
RESIDUAL VARIANCE: for the firs run you need to inform the value of 1.0, for the second you can pick the variance from the firs run’s output.
EFFECT: you will inform your first effect, in this example, Birth Year, which is in the column 3, and the word cross numer because is a number.
EFFECT: you should provide the next effect, in this example, sample date, as sample date has one non numeric character you should inform as cross alpha, in this example column 4.
EFFECT: now I’m providing my animal ID information, in this example column 1, and again cross alpha because has number and letters in the ID. I’m also informing that this effect is RANDOM, and that is my animal effect.
FILE: bellow this line I need to provide the pedigree file. Again, as I’m already in the directory which contain the pedigree file I only need to provide the file name.
FILE-POS: Here I’ll inform which columns should be considered in the pedigree file, in this situation, 1 2 3 0 0.
PED_DEPTH: Now we can inform the depth we want the software considers the pedigree, or if we leave 0 it will the maximum possible.
(CO) VARIANCES: Here you should provide the Variance/Co-variance matrix, like as for residual variance in the first run we set up to 0 in this example that we don´t have to imagine any co-variance, but if you know that exist variance among you effects you shoul set up XXX for ….
OPTION outcallrate: Save the call rate information on SNP markers in the file.
OPTION saveCleanSNPs: This option generates 4 new files. We assume snpfile as a marker file.
- snpfile_clean = new SNP marker file.
- snpfile_clean_XrefID = new cross-reference file.
- snpfile_SNPs_removed = a list of removed markers.
- snpfile_Animals_removed = a list of removed animals.
OPTION minfreq 0.01: Minimum allele frequency to retain the marker.
OPTION map_file snpmap.txt: This option will upload the SNP MAP
OPTION excludeCHR 30 31: This option will remove sexual chromosome that is the 30 and 31

To run any softwere from Blupf90 suit we will perform always in this way:

Go to the server you wanna run this analysis, for instance, grand

ssh grand

Now go to the directory you have created to run this analysis where that set of files are placed.

cd /home/bambrozi/2_CORTISOL/GWAS/BLUPF90

Make the renumF90 and BlupF90+ files executables

chmod +x renumf90
chmod +x blupf90+

Run renumF90

./renumf90

When you run the code above, it will ask you the name of your parameter card, for this step is renum_QC.par.

The command above will generate couple files, among them renf90.par

We modified renf90.par in:

renf90_DataClean.par
renf90_VarCompEst.par

The parameter card to perform the Quality Control is: /home/bambrozi/2_CORTISOL/GWAS/BLUPF90/renf90_DataClean.par

It will be run using the software pre preGS90 to generate the Clean Genotype and SNP_MAP files after Quality Control.

# BLUPF90 parameter file created by RENUMF90
DATAFILE
 renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        23 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   1.0000    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   1.0000    
OPTION SNP_file bruno_gntps_iid
OPTION outcallrate
OPTION saveCleanSNPs
OPTION minfreq 0.01
OPTION map_file snpmap.txt
OPTION excludeCHR 30 31

The second parameter card used for Variance Components Estimation (VCE) is the following: /home/bambrozi/2_CORTISOL/GWAS/BLUPF90/renf90_VarCompEst.par

It will be run using the software pre blupf90+ to generate the VCE.

# BLUPF90 parameter file created by RENUMF90
DATAFILE
 renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        23 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   1.0000    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   1.0000    
OPTION SNP_file bruno_gntps_iid_clean
OPTION no_quality_control
OPTION method VCE
OPTION origID
OPTION missing 9999
OPTION se_covar_function H2_1 g_3_3_1_1/(g_3_3_1_1+r_1_1)

OPTION SNP_file bruno_gntps_iid_clean: we are going to inform the genotype file generated in the previous step (the Quality Control). Blup will create an file with the same name that the original genotype file, and add the sufix **_clean**
OPTION no_quality_control we need to set up this option because we performed Quality Control in the previous step and now we don’t need that Blupf90+ perform again. Blupf90+ by default perform quality control, so if we do not want, we need to specify.
OPTION method: VCE (Variance Component Estimation).
OPTION OrigID: this will keep the original ID informed.
OPTION missing 9999: you are informing that missing values will appear as 9999
OPTION se_covar_function: H2_1 g_3_3_1_1/(g_3_3_1_1+r_1_1)
- H2_1: the name that your function will appear on the output files.
- g_3_3: you are asking for genetic variance estimation for the 3rd informed effect.
- **_1_1**: this effect is in the 1st column.
- /(g_3_3_1_1+r_1_1): to get the total phenotipic variance, you are summing to genetic variance the residual variance of the effect in column 1.

In the parameter card above, we remove the option for Quality Control and added options for Variance Components Estimation, for Missing data, for origID and for heritability estimation, but the MOST IMPORTANT PART is we need to change the OPTION SNP_FILE, replacing the original genotype file, for the “clean” version generated in the previous step.

The Variance Components will be placed in the file: /home/bambrozi/2_CORTISOL/GWAS/BLUPF90/blupf90.log

blupf90.log
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Now you should update you renf90_VarCompEst.par file with these informations from the .log file

Copy Residual Variance from blupf90.log and will paste on renf90_VarCompEst.par RANDOM_RESIDUAL_VALUES Copy Genetic variance for effect x from blupf90.log and will paste on renf90_VarCompEst.par (CO) VARIANCE

If the Residual Variance and Genetic variance for effect x didn’t change in your blupf90.log the analysis ended, but if this value vary, you should update again the renf90.par and run again blupf90+ until this values don’t change more.

blupf90.log
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Parameter card
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Now that we have the Variance components we go for the next step:

Prediction of SNP marker effects based on the GEBV
GWAS for High and Low cortisol animals

To do this, I’ll:

generate the Phenotype_FixEff file like bellow
generate the genotype file like bellow
Perform the QUALITY CONTROL for the new (71 samples) genotype file
Add the VCE from the 252 data set in the parameter card
Generate the GEBV
Run GWAS

9.3.5 Prediction of SNP marker effects based on the GEBV

9.3.6 GWAS for High and Low cortisol animals

Now we’ll run a new analysis using the Variance Components Estimation from the previous step to perform the GWAS.

To perform this first we need a Phenotype file, and a Genotype file only with the 71 animals (High=34 and Low=37)

9.3.6.1 Updating the files

9.3.6.1.1 Phenotype and Fixed Effects files

I used the code bellow to remove individuals with MEDIUM cortisol Phenotype

phenofix <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/fenofix.txt")


# Now we are gona remove the intermediary animals from pheno object
phenofix$Categorical <- ifelse(phenofix$V2 >= 956, "H", 
                            ifelse(phenofix$V2 <= 190.8, "L", "M"))
table(phenofix$Categorical)

phenofix <- phenofix[phenofix$Categorical != "M", ]

phenofix <- phenofix[, c("V1", "V2", "V3", "V4")]

write.table(phenofix, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/fenofix.txt", col.names = F, row.names = F, quote = F)

9.3.6.1.2 Genotype files

I used this command line bellow to remove the individuals with MEDIUM phenotypes.

awk 'NR==FNR{ids[$1]; next} $1 in ids' fenofix.txt bruno_gntps_iid > bruno_gntps_iid_71

9.3.6.2 Running renum_QC.par

/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/renum_QC.par

DATAFILE
fenofix.txt
TRAITS
2
FIELDS_PASSED TO OUTPUT

WEIGHT(S)

RESIDUAL_VARIANCE
77182
EFFECT
3 cross numer
EFFECT
4 cross alpha
EFFECT
1 cross alpha
RANDOM
animal
FILE
bruno_ped_iid_blup.txt
FILE_POS
1 2 3 0 0
SNP_FILE
bruno_gntps_iid_71
PED_DEPTH
0
(CO)VARIANCES
28212
OPTION outcallrate
OPTION saveCleanSNPs
OPTION minfreq 0.01
OPTION map_file snpmap.txt
OPTION excludeCHR 30 31

We modified renf90.par in 3 copies:

renf90_DataClean.par
renf90_ssGWAS1_Ginv.par
renf90_ssGWAS2_SNPeff.par

9.3.6.3 Running renf90_DataClean.par for Quality Control

To run the parameter card bellow we are going to use the software presGSf90 /home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/renf90_DataClean.par

# BLUPF90 parameter file created by RENUMF90
DATAFILE
 renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        22 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   77182.    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   28212.    
OPTION SNP_file bruno_gntps_iid_71
OPTION outcallrate
OPTION saveCleanSNPs
OPTION minfreq 0.01
OPTION map_file snpmap.txt
OPTION excludeCHR 30 31

9.3.6.4 Running renf90_ssGWAS1_Ginv.par for Ginv estimation

May be necessary to run the command bellow on the server Setting the stack size to “unlimited” allows the program to allocate memory for these large structures without hitting stack limits. By removing stack size limits, BLUPF90 is less likely to encounter segmentation faults or memory allocation issues that arise when the stack space is insufficient for the computations needed.

ulimit -s unlimited

The parameter card bellow we are going to run using the software blupf90+:

/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/renf90_ssGWAS1_Ginv.par

# BLUPF90 parameter file created by RENUMF90
DATAFILE
 renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        22 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   77182.    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   28212.    
OPTION SNP_file bruno_gntps_iid_71_clean
OPTION no_quality_control
OPTION origID
OPTION missing 9999
OPTION saveGInverse
OPTION saveA22Inverse
OPTION snp_p_value

9.3.6.5 Running renf90_ssGWAS2_SNPeff.par for GWAS

The parameter card bellow we are going to run using the software postGSf90:

# BLUPF90 parameter file created by RENUMF90
DATAFILE
 renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        22 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   77182.    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   28212.    
OPTION SNP_file bruno_gntps_iid_71_clean
OPTION origID
OPTION no_quality_control
OPTION readGInverse
OPTION readA22Inverse
OPTION map_file snpmap.txt_clean
OPTION snp_p_value
OPTION Manhattan_plot_R
OPTION Manhattan_plot

This code will generate several files, among them:

chrsnp_pval:
- Column 1: trait
- Column 2: effect
- Column 3: -log10(p-value)
- Column 4: SNP
- Column 5: Chromosome
- Column 6: Position in bp
Pft1e3.R: a r-code to generate the Manhattan plot in R using the chrsnp_pval

9.3.6.6 Manhattan Plots for BLUPF90 GWAS

9.3.6.6.1 Genome Independent Segment

To make the Manhattan Plot considering Genome Independent Segment we should run the code bellow. This code has part of the code in the file Pft1e3.R

Genome_Assembly_ARS_UCD_1_2 <- read_tsv("/home/bambrozi/2_CORTISOL/GWAS/sequence_report_ARS-UCD1_2.tsv")

library(dplyr)
# Filter the rows and sum the Seq length column
# Assuming your data frame is named Genome_Assembly_ARS_UCD_1_2
L <- Genome_Assembly_ARS_UCD_1_2 %>%
  filter(`UCSC style name` %in% paste0("chr", 1:29)) %>%
  summarise(total_length = sum(`Seq length`)) %>%
  pull(total_length)

# Converting bases to Morgan (1Mb = 1cM (0,01 Morgan))
L_M <- L/10^8

# The Ne measure is based on the article bellow:
Ne <- 66 #(Makanjoula et al., 2020)

NeL <- Ne*L_M

# This is the number of independent segment in the genome.
Me <- (2*NeL)/log10(NeL)


# Calculate Bonferroni threshold (already done)
bonf <- -log10(0.05 / Me)


setwd("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO")
# Read in and process data for Manhattan plot
yyy1 <- read.table("chrsnp_pval")
yyy <- yyy1[order(yyy1$V4), ]
zzz <- yyy[which(yyy$V1 == 1 & yyy$V2 == 3), ]
n <- nrow(zzz)
y <- zzz[, 4]
x <- zzz[, 3]
chr1 <- zzz[, 5]
chr <- NULL
pos <- NULL

for (i in unique(yyy$V5)) {
  zz <- yyy[yyy$V5 == i, ]
  key <- zz$V4
  medio <- round(nrow(zz) / 2, 0)
  z <- key[medio]
  pos <- c(pos, z)
}

chrn <- unique(yyy$V5)
one <- which(chr1 %% 4 == 0)
two <- which(chr1 %% 4 == 1)
three <- which(chr1 %% 4 == 2)
four <- which(chr1 %% 4 == 3)
chr[one] <- "darkgoldenrod"
chr[two] <- "darkorchid"
chr[three] <- "blue"
chr[four] <- "forestgreen"

# Create Manhattan plot with Bonferroni line and legend
pdf(file = "Pft1e3_manplot_with_bonf_ind_seg.pdf", family = "sans", height = 27.8, width = 50, pointsize = 20, bg = "white")
par(mfrow = c(1, 1), family = "sans", cex = 1.5, font = 2)
plot(y, x, xaxt = "n", main = "Manhattan Plot SNP p_value - Trait: 1 Effect: 3", xlab = "", ylab = "-log10(p-value)", pch = 20, xlim = c(1, n), ylim = c(0, max(x)), col = chr)

# Add Bonferroni line
abline(h = bonf, col = "red", lwd = 2, lty = 2)

# Add legend for Bonferroni line
legend("topright", legend = "Bonferroni correction for genome independent segments", col = "red", lwd = 2, lty = 2, cex = 1)

axis(1, at = pos, labels = chrn)
dev.off()

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9.3.6.7 Get rsID

For additional analysis like Variant Effect Prediction (VEP) we need the rsID, to get the rsID we use the software SNPChimp which requires SNP_names, but the BLUPF90 output have only the Chromosome and Position of the SNPs, so we are going to perfome these two steps to get one file with t he significant SNPs + SNP_name + rsID

9.3.6.7.1 Step 01 = Bring the SNP name to GWAS output

For this analysis we have to build this new sheet bringing SNP ID from snpmap.txt

gwas = read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/chrsnp_pval")
colnames(gwas) <- c("V1", "V2", "LOG_P", "SNP", "CHR", "BP")
gwas <- filter(gwas, LOG_P >= bonf)

snpmap <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/snpmap.txt", header = T)

# Filter snpmap to only include rows that match the CHR and BP values in out_genes
filtered_snpmap <- snpmap[snpmap$CHR %in% gwas$CHR & snpmap$POS %in% gwas$BP, ]

# Merge the filtered snpmap with out_genes
gwas_snpname <- merge(gwas, filtered_snpmap[, c("CHR", "POS", "SNP_ID")], 
                   by.x = c("CHR", "BP"), by.y = c("CHR", "POS"), 
                   all.x = TRUE)

gwas_snpname <- gwas_snpname[,c("CHR", "BP", "LOG_P", "SNP_ID")]

write.csv(gwas_snpname, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/chrsnp_pval_SNPid_ind_seg_sig_BLUPF90.csv")

X	CHR	BP	LOG_P	SNP_ID
1	11	19779915	4.828190	Hapmap55558-rs29013980
2	12	17477322	4.417576	BTB-00488482
3	14	15929822	5.467439	ARS-BFGL-NGS-82859
4	15	33066384	5.041271	BTB-00594449
5	15	57930281	4.514377	ARS-BFGL-NGS-30515
6	17	46487068	4.547766	ARS-BFGL-NGS-12510
7	17	47884497	6.622354	ARS-BFGL-NGS-87412
8	17	48800954	5.268940	ARS-BFGL-NGS-112149
9	2	118820218	4.420962	Hapmap53065-rs29026778
10	2	41602429	4.497977	BTB-00096979
11	2	41670655	4.377284	Hapmap48777-BTA-47434
12	20	60365668	4.603817	BTB-01341053
13	20	65351653	4.891206	ARS-BFGL-NGS-91119
14	21	4055731	4.493938	ARS-BFGL-NGS-112210
15	23	44822126	4.316434	ARS-BFGL-NGS-115605
16	24	857728	4.458018	Hapmap47669-BTA-59022
17	28	20016672	5.130557	ARS-BFGL-NGS-116552
18	28	35807366	4.695875	ARS-BFGL-NGS-71077
19	29	33039863	4.933140	ARS-BFGL-NGS-41631
20	3	110866523	4.815943	ARS-BFGL-NGS-118207
21	3	111526170	4.760351	ARS-BFGL-NGS-74948
22	3	111730561	4.390707	BTB-01641394
23	3	111751663	4.390707	ARS-BFGL-NGS-37809
24	3	111772736	5.088738	ARS-BFGL-NGS-25298
25	4	103979600	5.591006	ARS-BFGL-NGS-110705
26	4	107787202	5.375628	ARS-BFGL-NGS-45265
27	4	109568557	4.709882	Hapmap48062-BTA-72409
28	4	110053134	5.245598	UA-IFASA-2147
29	4	24239851	4.641241	Hapmap60681-rs29013301
30	4	25933587	4.352451	Hapmap50554-BTA-107048
31	4	26511448	4.537662	Hapmap59011-rs29027498
32	4	27021431	5.045447	Hapmap59743-rs29017061
33	4	27094376	5.485063	BTB-00170785
34	4	50755462	5.015905	ARS-BFGL-NGS-12139
35	4	7873471	4.610767	Hapmap60503-rs29018741
36	4	95650788	4.825103	Hapmap58854-rs29023486
37	6	36184467	4.758110	Hapmap23854-BTC-062412
38	7	110306791	4.609691	BTB-01148543

9.3.6.7.2 Step 02 = Bring the rsID to the file with SNP name.

After get the rsID from SNPCHIMP I produced this table with the code bellow:

rsid <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/SNPchimp_result_1723415732_BLUPF90GWAS.tsv", header = T)

merged <- merge(rsid, gwas_snpname, by.x ="SNP_name", by.y ="SNP_ID")

colnames(merged)

merged <- merged[,c("SNP_name", "rs", "CHR", "BP", "LOG_P")]

colnames(merged) <- c("SNP_name", "rsID", "CHR", "BP", "LOG_P")

write.csv(merged, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/gwas_ind_seg_sig_SNPname_rsID.csv")

X	SNP_name	rsID	CHR	BP	LOG_P
1	ARS-BFGL-NGS-110705	rs110079750	4	103979600	5.591006
2	ARS-BFGL-NGS-112149	rs109276211	17	48800954	5.268940
3	ARS-BFGL-NGS-112210	rs109631116	21	4055731	4.493938
4	ARS-BFGL-NGS-115605	rs42029843	23	44822126	4.316434
5	ARS-BFGL-NGS-116552	rs110428837	28	20016672	5.130557
6	ARS-BFGL-NGS-118207	rs110081798	3	110866523	4.815943
7	ARS-BFGL-NGS-12139	rs110160157	4	50755462	5.015905
8	ARS-BFGL-NGS-12510	rs110038841	17	46487068	4.547766
9	ARS-BFGL-NGS-25298	rs109868537	3	111772736	5.088738
10	ARS-BFGL-NGS-30515	rs110565206	15	57930281	4.514377
11	ARS-BFGL-NGS-37809	rs42751504	3	111751663	4.390707
12	ARS-BFGL-NGS-41631	rs110121846	29	33039863	4.933140
13	ARS-BFGL-NGS-45265	rs110935391	4	107787202	5.375628
14	ARS-BFGL-NGS-71077	rs109584097	28	35807366	4.695875
15	ARS-BFGL-NGS-74948	rs41585925	3	111526170	4.760351
16	ARS-BFGL-NGS-82859	rs110506037	14	15929822	5.467439
17	ARS-BFGL-NGS-87412	rs109273103	17	47884497	6.622354
18	ARS-BFGL-NGS-91119	rs109575643	20	65351653	4.891206
19	BTB-00096979	rs43305418	2	41602429	4.497977
20	BTB-00170785	rs43377276	4	27094376	5.485063
21	BTB-00488482	rs43691687	12	17477322	4.417576
22	BTB-00594449	rs41764450	15	33066384	5.041271
23	BTB-01148543	rs42305073	7	110306791	4.609691
24	BTB-01341053	rs42462935	20	60365668	4.603817
25	BTB-01641394	rs42752353	3	111730561	4.390707
26	Hapmap23854-BTC-062412	rs81154019	6	36184467	4.758110
27	Hapmap47669-BTA-59022	rs41645754	24	857728	4.458018
28	Hapmap48062-BTA-72409	rs41566051	4	109568557	4.709882
29	Hapmap48777-BTA-47434	rs41636137	2	41670655	4.377284
30	Hapmap50554-BTA-107048	rs41615935	4	25933587	4.352451
31	Hapmap53065-rs29026778	rs29026778	2	118820218	4.420962
32	Hapmap55558-rs29013980	rs29013980	11	19779915	4.828190
33	Hapmap58854-rs29023486	rs29023486	4	95650788	4.825103
34	Hapmap59011-rs29027498	rs29027498	4	26511448	4.537662
35	Hapmap59743-rs29017061	rs29017061	4	27021431	5.045447
36	Hapmap60503-rs29018741	rs29018741	4	7873471	4.610767
37	Hapmap60681-rs29013301	rs29013301	4	24239851	4.641241
38	UA-IFASA-2147	rs29012492	4	110053134	5.245598

9.3.7 BLUPF90+ GALLO

# GALLO

#import a QTL annotation file
qtl_UCD1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Animal_QTLdb_release53_cattleARS_UCD1.gff.gz",file_type="gff")

#import a gene annotation file
gene_UDC1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Bos_taurus.ARS-UCD1.2.110.gtf.gz",file_type="gtf")

#import MARKER files = the GWAS output
gwas = read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/gwas_ind_seg_sig_SNPname_rsID.csv")
colnames(gwas) <- c("X", "SNP", "rsID", "CHR", "BP", "LOG_P")


#FINDING GENES AND QTLs ARROUND THE MARKER

#FINDING GENES
out.genes <- find_genes_qtls_around_markers(db_file= gene_UDC1_2, 
                                            marker_file= gwas, 
                                            method = "gene",
                                            marker = "snp", 
                                            interval = 50000, 
                                            nThreads = NULL)

write.csv(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_genes_50k_pvalue.csv")

#FINDING QTLs

out.qtl <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                          marker_file= gwas, 
                                          method = "qtl",
                                          marker = "snp", 
                                          interval = 50000, 
                                          nThreads = NULL)


write.table(out.qtl, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_qtl_50k_pvalue.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

library(tidyverse)
out.qtl.clean <- select(out.qtl, c("SNP", "rsID", "CHR", "QTL_type", "start_pos", "end_pos","QTL_ID"))
write.csv(out.qtl.clean, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_qtl_50k_pvalue_clean.csv")

The GALLO output are bellow:

For GENES

X.1	X	SNP	rsID	CHR	BP	LOG_P	chr	start_pos	end_pos	width	strand	gene_id	gene_name	gene_biotype
1	34	Hapmap59011-rs29027498	rs29027498	4	26511448	4.537662	4	26324012	26469689	145678	-	ENSBTAG00000014074	SNX13	protein_coding
2	20	BTB-00170785	rs43377276	4	27094376	5.485063	4	27093041	27667961	574921	+	ENSBTAG00000003808	HDAC9	protein_coding
3	33	Hapmap58854-rs29023486	rs29023486	4	95650788	4.825103	4	95631850	95631921	72	-	ENSBTAG00000045386	bta-mir-320b	miRNA
4	1	ARS-BFGL-NGS-110705	rs110079750	4	103979600	5.591006	4	103877533	103947944	70412	-	ENSBTAG00000004799	DENND2A	protein_coding
5	1	ARS-BFGL-NGS-110705	rs110079750	4	103979600	5.591006	4	103994401	104008963	14563	+	ENSBTAG00000020484	ADCK2	protein_coding
6	1	ARS-BFGL-NGS-110705	rs110079750	4	103979600	5.591006	4	104013524	104019157	5634	+	ENSBTAG00000021759	NDUFB2	protein_coding
7	1	ARS-BFGL-NGS-110705	rs110079750	4	103979600	5.591006	4	104027627	104194547	166921	-	ENSBTAG00000021761	BRAF	protein_coding
8	13	ARS-BFGL-NGS-45265	rs110935391	4	107787202	5.375628	4	107701708	108020989	319282	+	ENSBTAG00000054424	NA	protein_coding
9	13	ARS-BFGL-NGS-45265	rs110935391	4	107787202	5.375628	4	107718449	108101163	382715	-	ENSBTAG00000017676	TPK1	protein_coding
10	28	Hapmap48062-BTA-72409	rs41566051	4	109568557	4.709882	4	109526182	109526350	169	-	ENSBTAG00000053666	NA	misc_RNA
11	36	Hapmap60503-rs29018741	rs29018741	4	7873471	4.610767	4	7867490	7867588	99	-	ENSBTAG00000045389	U6	snRNA
12	7	ARS-BFGL-NGS-12139	rs110160157	4	50755462	5.015905	4	50743789	50957593	213805	-	ENSBTAG00000006589	CFTR	protein_coding
13	8	ARS-BFGL-NGS-12510	rs110038841	17	46487068	4.547766	17	46406767	46715519	308753	+	ENSBTAG00000009797	RIMBP2	protein_coding
14	17	ARS-BFGL-NGS-87412	rs109273103	17	47884497	6.622354	17	47426664	48084896	658233	+	ENSBTAG00000002950	TMEM132D	protein_coding
15	2	ARS-BFGL-NGS-112149	rs109276211	17	48800954	5.268940	17	48417339	48868560	451222	-	ENSBTAG00000046256	TMEM132C	protein_coding
16	3	ARS-BFGL-NGS-112210	rs109631116	21	4055731	4.493938	21	3866347	4146441	280095	-	ENSBTAG00000013422	GABRB3	protein_coding
17	4	ARS-BFGL-NGS-115605	rs42029843	23	44822126	4.316434	23	44802116	44835671	33556	-	ENSBTAG00000010256	TMEM170B	protein_coding
18	14	ARS-BFGL-NGS-71077	rs109584097	28	35807366	4.695875	28	35805434	35823873	18440	+	ENSBTAG00000021416	PRXL2A	protein_coding
19	14	ARS-BFGL-NGS-71077	rs109584097	28	35807366	4.695875	28	35848163	35904370	56208	+	ENSBTAG00000003907	TSPAN14	protein_coding
20	6	ARS-BFGL-NGS-118207	rs110081798	3	110866523	4.815943	3	110769753	110827938	58186	+	ENSBTAG00000013867	DLGAP3	protein_coding
21	6	ARS-BFGL-NGS-118207	rs110081798	3	110866523	4.815943	3	110835276	110839536	4261	+	ENSBTAG00000014235	SMIM12	protein_coding
22	6	ARS-BFGL-NGS-118207	rs110081798	3	110866523	4.815943	3	110888198	110890904	2707	-	ENSBTAG00000013881	GJA4	protein_coding
23	6	ARS-BFGL-NGS-118207	rs110081798	3	110866523	4.815943	3	110900183	110905849	5667	-	ENSBTAG00000012584	GJB3	protein_coding
24	15	ARS-BFGL-NGS-74948	rs41585925	3	111526170	4.760351	3	111552563	111568286	15724	-	ENSBTAG00000020798	C3H1orf94	protein_coding
25	25	BTB-01641394	rs42752353	3	111730561	4.390707	3	111603940	112289188	685249	+	ENSBTAG00000005784	CSMD2	protein_coding
26	11	ARS-BFGL-NGS-37809	rs42751504	3	111751663	4.390707	3	111603940	112289188	685249	+	ENSBTAG00000005784	CSMD2	protein_coding
27	9	ARS-BFGL-NGS-25298	rs109868537	3	111772736	5.088738	3	111603940	112289188	685249	+	ENSBTAG00000005784	CSMD2	protein_coding
28	22	BTB-00594449	rs41764450	15	33066384	5.041271	15	32820702	33050885	230184	-	ENSBTAG00000054820	NA	lncRNA
29	12	ARS-BFGL-NGS-41631	rs110121846	29	33039863	4.933140	29	32993975	33024632	30658	-	ENSBTAG00000003176	JAM3	protein_coding
30	12	ARS-BFGL-NGS-41631	rs110121846	29	33039863	4.933140	29	33036823	33044208	7386	-	ENSBTAG00000052082	NA	lncRNA
31	16	ARS-BFGL-NGS-82859	rs110506037	14	15929822	5.467439	14	15865228	15995482	130255	-	ENSBTAG00000013537	FER1L6	protein_coding
32	16	ARS-BFGL-NGS-82859	rs110506037	14	15929822	5.467439	14	15914441	15917042	2602	+	ENSBTAG00000048373	NA	protein_coding
33	18	ARS-BFGL-NGS-91119	rs109575643	20	65351653	4.891206	20	65311530	65343117	31588	-	ENSBTAG00000009401	MTRR	protein_coding
34	18	ARS-BFGL-NGS-91119	rs109575643	20	65351653	4.891206	20	65343172	65354775	11604	+	ENSBTAG00000009400	FASTKD3	protein_coding
35	18	ARS-BFGL-NGS-91119	rs109575643	20	65351653	4.891206	20	65368557	65384042	15486	+	ENSBTAG00000055034	CFAP90	protein_coding
36	18	ARS-BFGL-NGS-91119	rs109575643	20	65351653	4.891206	20	65388705	65842986	454282	-	ENSBTAG00000019210	ADCY2	protein_coding
37	31	Hapmap53065-rs29026778	rs29026778	2	118820218	4.420962	2	118803760	118824701	20942	+	ENSBTAG00000030718	SPATA3	protein_coding
38	31	Hapmap53065-rs29026778	rs29026778	2	118820218	4.420962	2	118831062	118836777	5716	+	ENSBTAG00000054626	NA	lncRNA
39	31	Hapmap53065-rs29026778	rs29026778	2	118820218	4.420962	2	118846915	118854132	7218	+	ENSBTAG00000026710	C2H2orf72	protein_coding
40	31	Hapmap53065-rs29026778	rs29026778	2	118820218	4.420962	2	118865174	118946608	81435	+	ENSBTAG00000005119	PSMD1	protein_coding
41	29	Hapmap48777-BTA-47434	rs41636137	2	41670655	4.377284	2	41676135	42259624	583490	-	ENSBTAG00000005562	GALNT13	protein_coding
42	23	BTB-01148543	rs42305073	7	110306791	4.609691	7	110296879	110565532	268654	+	ENSBTAG00000035662	CAMK4	protein_coding
43	26	Hapmap23854-BTC-062412	rs81154019	6	36184467	4.758110	6	36052150	36198478	146329	-	ENSBTAG00000010120	HERC3	protein_coding
44	27	Hapmap47669-BTA-59022	rs41645754	24	857728	4.458018	24	779627	866891	87265	-	ENSBTAG00000000656	NFATC1	protein_coding
45	27	Hapmap47669-BTA-59022	rs41645754	24	857728	4.458018	24	879074	1029386	150313	-	ENSBTAG00000001224	ATP9B	protein_coding
46	32	Hapmap55558-rs29013980	rs29013980	11	19779915	4.828190	11	19680235	19765027	84793	-	ENSBTAG00000001114	PRKD3	protein_coding
47	32	Hapmap55558-rs29013980	rs29013980	11	19779915	4.828190	11	19792170	19822758	30589	+	ENSBTAG00000013923	QPCT	protein_coding

FOR QTLs

X	SNP	rsID	CHR	QTL_type	start_pos	end_pos	QTL_ID
1	Hapmap60503-rs29018741	rs29018741	4	Milk	7839854	7839858	118382
2	Hapmap60503-rs29018741	rs29018741	4	Milk	7851689	7851693	118383
3	Hapmap60503-rs29018741	rs29018741	4	Milk	7878682	7878686	113676
4	Hapmap60503-rs29018741	rs29018741	4	Milk	7878682	7878686	118072
5	Hapmap60681-rs29013301	rs29013301	4	Meat_and_Carcass	24248260	24248264	228128
6	Hapmap60681-rs29013301	rs29013301	4	Reproduction	24279008	24279012	212516
7	Hapmap60681-rs29013301	rs29013301	4	Meat_and_Carcass	24288010	24288014	107768
8	Hapmap60681-rs29013301	rs29013301	4	Meat_and_Carcass	24288010	24288014	107796
9	Hapmap59011-rs29027498	rs29027498	4	Exterior	26511446	26511450	66124
10	Hapmap59743-rs29017061	rs29017061	4	Production	27014514	27014518	65173
11	ARS-BFGL-NGS-12139	rs110160157	4	Meat_and_Carcass	50781007	50781011	228112
12	ARS-BFGL-NGS-110705	rs110079750	4	Health	103949847	103949851	96218
13	ARS-BFGL-NGS-110705	rs110079750	4	Meat_and_Carcass	104013536	104013540	231600
14	ARS-BFGL-NGS-45265	rs110935391	4	Reproduction	107749162	107749166	106782
15	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46449873	46449877	162016
16	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46449873	46449877	163641
17	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46452018	46452022	162017
18	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46452018	46452022	163642
19	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46455275	46455279	163230
20	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46455275	46455279	163643
21	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46457135	46457139	163231
22	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46457135	46457139	163644
23	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46460742	46460746	163159
24	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46460742	46460746	163583
25	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46463937	46463941	163232
26	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46463937	46463941	163645
27	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46474328	46474332	162015
28	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46474328	46474332	163640
29	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46474997	46475001	162014
30	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46474997	46475001	163639
31	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46476243	46476247	162013
32	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46476243	46476247	163638
33	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46476816	46476820	162012
34	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46476816	46476820	163637
35	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46480499	46480503	161948
36	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46480499	46480503	162037
37	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46481110	46481114	161941
38	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46481110	46481114	162030
39	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46481709	46481713	162019
40	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46481709	46481713	162100
41	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46482674	46482678	162020
42	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46482674	46482678	162101
43	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46483484	46483488	162021
44	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46483484	46483488	162102
45	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46529284	46529288	163162
46	ARS-BFGL-NGS-12510	rs110038841	17	Milk	46529284	46529288	163584
47	ARS-BFGL-NGS-87412	rs109273103	17	Milk	47837023	47837027	162764
48	ARS-BFGL-NGS-87412	rs109273103	17	Milk	47844960	47844964	162876
49	ARS-BFGL-NGS-87412	rs109273103	17	Milk	47851806	47851810	162921
50	ARS-BFGL-NGS-87412	rs109273103	17	Milk	47855252	47855256	162648
51	ARS-BFGL-NGS-87412	rs109273103	17	Production	47856400	47856404	23869
52	ARS-BFGL-NGS-87412	rs109273103	17	Production	47856400	47856404	68904
53	ARS-BFGL-NGS-87412	rs109273103	17	Exterior	47856400	47856404	102064
54	ARS-BFGL-NGS-87412	rs109273103	17	Exterior	47884495	47884499	102065
55	ARS-BFGL-NGS-87412	rs109273103	17	Milk	47924476	47924480	163030
56	ARS-BFGL-NGS-87412	rs109273103	17	Milk	47924476	47924480	163517
57	ARS-BFGL-NGS-87412	rs109273103	17	Milk	47926919	47926923	162858
58	ARS-BFGL-NGS-87412	rs109273103	17	Milk	47926919	47926923	163422
59	ARS-BFGL-NGS-112149	rs109276211	17	Milk	48759668	48759672	63966
60	ARS-BFGL-NGS-112210	rs109631116	21	Reproduction	4074797	4074801	145524
61	ARS-BFGL-NGS-112210	rs109631116	21	Reproduction	4079204	4079208	145458
62	ARS-BFGL-NGS-112210	rs109631116	21	Reproduction	4079247	4079251	145445
63	ARS-BFGL-NGS-112210	rs109631116	21	Reproduction	4085667	4085671	145389
64	ARS-BFGL-NGS-112210	rs109631116	21	Reproduction	4085667	4085671	146710
65	ARS-BFGL-NGS-112210	rs109631116	21	Reproduction	4085817	4085821	146703
66	ARS-BFGL-NGS-115605	rs42029843	23	Milk	44787659	44787663	254748
67	ARS-BFGL-NGS-115605	rs42029843	23	Milk	44789048	44789052	254706
68	ARS-BFGL-NGS-115605	rs42029843	23	Meat_and_Carcass	44807487	44807491	226965
69	ARS-BFGL-NGS-115605	rs42029843	23	Meat_and_Carcass	44807487	44807491	229458
70	ARS-BFGL-NGS-115605	rs42029843	23	Meat_and_Carcass	44807487	44807491	234754
71	ARS-BFGL-NGS-115605	rs42029843	23	Milk	44808987	44808991	254767
72	ARS-BFGL-NGS-115605	rs42029843	23	Milk	44830780	44830784	254833
73	ARS-BFGL-NGS-115605	rs42029843	23	Milk	44834127	44834131	254725
74	ARS-BFGL-NGS-115605	rs42029843	23	Milk	44845256	44845260	254688
75	ARS-BFGL-NGS-115605	rs42029843	23	Milk	44848146	44848150	254689
76	ARS-BFGL-NGS-115605	rs42029843	23	Milk	44859653	44859657	254769
77	ARS-BFGL-NGS-116552	rs110428837	28	Milk	20016670	20016674	112226
78	ARS-BFGL-NGS-116552	rs110428837	28	Milk	20016670	20016674	118869
79	ARS-BFGL-NGS-116552	rs110428837	28	Milk	20017763	20017767	112227
80	ARS-BFGL-NGS-116552	rs110428837	28	Milk	20017763	20017767	118870
81	ARS-BFGL-NGS-116552	rs110428837	28	Milk	20045011	20045015	118664
82	ARS-BFGL-NGS-116552	rs110428837	28	Milk	20060184	20060188	118665
83	ARS-BFGL-NGS-71077	rs109584097	28	Reproduction	35807364	35807368	138601
84	ARS-BFGL-NGS-71077	rs109584097	28	Reproduction	35807364	35807368	138602
85	ARS-BFGL-NGS-118207	rs110081798	3	Meat_and_Carcass	110866521	110866525	152332
86	ARS-BFGL-NGS-74948	rs41585925	3	Health	111487002	111487006	137202
87	ARS-BFGL-NGS-74948	rs41585925	3	Health	111487002	111487006	170274
88	ARS-BFGL-NGS-74948	rs41585925	3	Health	111509252	111509256	170275
89	ARS-BFGL-NGS-74948	rs41585925	3	Health	111515878	111515882	170276
90	BTB-01641394	rs42752353	3	Reproduction	111708234	111708238	30008
91	ARS-BFGL-NGS-37809	rs42751504	3	Reproduction	111708234	111708238	30008
92	BTB-01641394	rs42752353	3	Reproduction	111708234	111708238	30244
93	ARS-BFGL-NGS-37809	rs42751504	3	Reproduction	111708234	111708238	30244
94	BTB-01641394	rs42752353	3	Meat_and_Carcass	111708234	111708238	152258
95	ARS-BFGL-NGS-37809	rs42751504	3	Meat_and_Carcass	111708234	111708238	152258
96	BTB-01641394	rs42752353	3	Meat_and_Carcass	111717521	111717525	225527
97	ARS-BFGL-NGS-37809	rs42751504	3	Meat_and_Carcass	111717521	111717525	225527
98	BTB-00594449	rs41764450	15	Production	33066382	33066386	68791
99	BTB-00594449	rs41764450	15	Meat_and_Carcass	33107568	33107572	228743
100	ARS-BFGL-NGS-30515	rs110565206	15	Milk	57892792	57892796	242339
101	ARS-BFGL-NGS-30515	rs110565206	15	Milk	57914040	57914044	248429
102	ARS-BFGL-NGS-30515	rs110565206	15	Milk	57926776	57926780	242396
103	ARS-BFGL-NGS-30515	rs110565206	15	Milk	57926776	57926780	248428
104	ARS-BFGL-NGS-30515	rs110565206	15	Milk	57949776	57949780	242391
105	ARS-BFGL-NGS-41631	rs110121846	29	Production	33039861	33039865	69450
106	ARS-BFGL-NGS-41631	rs110121846	29	Production	33039861	33039865	69451
107	ARS-BFGL-NGS-82859	rs110506037	14	Milk	15895119	15895123	25606
108	ARS-BFGL-NGS-82859	rs110506037	14	Milk	15895119	15895123	26355
109	ARS-BFGL-NGS-82859	rs110506037	14	Meat_and_Carcass	15895119	15895123	36989
110	ARS-BFGL-NGS-82859	rs110506037	14	Milk	15904094	15904098	104606
111	ARS-BFGL-NGS-82859	rs110506037	14	Milk	15905538	15905542	104607
112	ARS-BFGL-NGS-82859	rs110506037	14	Exterior	15929820	15929824	220989
113	ARS-BFGL-NGS-82859	rs110506037	14	Production	15964283	15964287	122755
114	ARS-BFGL-NGS-82859	rs110506037	14	Production	15964283	15964287	123539
115	ARS-BFGL-NGS-82859	rs110506037	14	Production	15965017	15965021	122756
116	ARS-BFGL-NGS-82859	rs110506037	14	Production	15965017	15965021	123540
117	ARS-BFGL-NGS-82859	rs110506037	14	Production	15967515	15967519	122757
118	ARS-BFGL-NGS-82859	rs110506037	14	Production	15967515	15967519	123541
119	ARS-BFGL-NGS-82859	rs110506037	14	Production	15969384	15969388	122758
120	ARS-BFGL-NGS-82859	rs110506037	14	Production	15969384	15969388	123542
121	ARS-BFGL-NGS-82859	rs110506037	14	Production	15972216	15972220	122759
122	ARS-BFGL-NGS-82859	rs110506037	14	Production	15972216	15972220	123543
123	ARS-BFGL-NGS-82859	rs110506037	14	Production	15977822	15977826	122760
124	ARS-BFGL-NGS-82859	rs110506037	14	Production	15977822	15977826	123544
125	ARS-BFGL-NGS-91119	rs109575643	20	Milk	65321359	65321363	176120
126	ARS-BFGL-NGS-91119	rs109575643	20	Milk	65350145	65350149	69962
127	ARS-BFGL-NGS-91119	rs109575643	20	Milk	65352215	65352219	69963
128	ARS-BFGL-NGS-91119	rs109575643	20	Milk	65357974	65357978	69964
129	ARS-BFGL-NGS-91119	rs109575643	20	Milk	65363596	65363600	69626
130	ARS-BFGL-NGS-91119	rs109575643	20	Health	65367232	65367236	167647
131	BTB-00096979	rs43305418	2	Health	41584083	41584087	32371
132	BTB-00096979	rs43305418	2	Milk	41646649	41646653	113699
133	Hapmap48777-BTA-47434	rs41636137	2	Milk	41646649	41646653	113699
134	Hapmap48777-BTA-47434	rs41636137	2	Milk	41668170	41668174	113700
135	Hapmap48777-BTA-47434	rs41636137	2	Meat_and_Carcass	41676143	41676147	154062
136	Hapmap48777-BTA-47434	rs41636137	2	Reproduction	41693862	41693866	29899
137	Hapmap53065-rs29026778	rs29026778	2	Milk	118779183	118779187	200902
138	Hapmap53065-rs29026778	rs29026778	2	Milk	118779985	118779989	202500
139	Hapmap53065-rs29026778	rs29026778	2	Milk	118781186	118781190	202905
140	Hapmap53065-rs29026778	rs29026778	2	Milk	118785252	118785256	201638
141	Hapmap53065-rs29026778	rs29026778	2	Milk	118789209	118789213	202885
142	Hapmap53065-rs29026778	rs29026778	2	Reproduction	118820216	118820220	39599
143	Hapmap53065-rs29026778	rs29026778	2	Reproduction	118820216	118820220	39600
144	Hapmap53065-rs29026778	rs29026778	2	Reproduction	118820216	118820220	39601
145	Hapmap53065-rs29026778	rs29026778	2	Exterior	118820216	118820220	39602
146	Hapmap53065-rs29026778	rs29026778	2	Exterior	118820216	118820220	39603
147	Hapmap53065-rs29026778	rs29026778	2	Milk	118820216	118820220	39604
148	Hapmap53065-rs29026778	rs29026778	2	Production	118820216	118820220	39605
149	Hapmap53065-rs29026778	rs29026778	2	Exterior	118820216	118820220	39606
150	Hapmap53065-rs29026778	rs29026778	2	Milk	118820216	118820220	39607
151	Hapmap53065-rs29026778	rs29026778	2	Production	118820216	118820220	39608
152	Hapmap53065-rs29026778	rs29026778	2	Production	118820216	118820220	39609
153	Hapmap53065-rs29026778	rs29026778	2	Exterior	118820216	118820220	39610
154	Hapmap53065-rs29026778	rs29026778	2	Exterior	118820216	118820220	39611
155	Hapmap53065-rs29026778	rs29026778	2	Production	118820216	118820220	39612
156	Hapmap53065-rs29026778	rs29026778	2	Reproduction	118820216	118820220	39613
157	Hapmap53065-rs29026778	rs29026778	2	Health	118820216	118820220	39614
158	Hapmap53065-rs29026778	rs29026778	2	Exterior	118820216	118820220	39615
159	Hapmap53065-rs29026778	rs29026778	2	Exterior	118820216	118820220	39616
160	Hapmap53065-rs29026778	rs29026778	2	Reproduction	118820216	118820220	125235
161	Hapmap23854-BTC-062412	rs81154019	6	Milk	36141496	36141500	244115
162	Hapmap23854-BTC-062412	rs81154019	6	Milk	36141496	36141500	246435
163	Hapmap23854-BTC-062412	rs81154019	6	Milk	36141496	36141500	250700
164	Hapmap23854-BTC-062412	rs81154019	6	Production	36143407	36143411	190189
165	Hapmap23854-BTC-062412	rs81154019	6	Production	36143929	36143933	187391
166	Hapmap23854-BTC-062412	rs81154019	6	Production	36145023	36145027	183480
167	Hapmap23854-BTC-062412	rs81154019	6	Production	36145023	36145027	190272
168	Hapmap23854-BTC-062412	rs81154019	6	Production	36145254	36145258	182513
169	Hapmap23854-BTC-062412	rs81154019	6	Production	36145254	36145258	186692
170	Hapmap23854-BTC-062412	rs81154019	6	Production	36147416	36147420	187702
171	Hapmap23854-BTC-062412	rs81154019	6	Production	36147705	36147709	186912
172	Hapmap23854-BTC-062412	rs81154019	6	Production	36148373	36148377	182603
173	Hapmap23854-BTC-062412	rs81154019	6	Production	36148373	36148377	188134
174	Hapmap23854-BTC-062412	rs81154019	6	Production	36149039	36149043	186551
175	Hapmap23854-BTC-062412	rs81154019	6	Production	36150874	36150878	183765
176	Hapmap23854-BTC-062412	rs81154019	6	Production	36150874	36150878	186794
177	Hapmap23854-BTC-062412	rs81154019	6	Production	36151323	36151327	183528
178	Hapmap23854-BTC-062412	rs81154019	6	Production	36151323	36151327	187598
179	Hapmap23854-BTC-062412	rs81154019	6	Production	36153011	36153015	188352
180	Hapmap23854-BTC-062412	rs81154019	6	Production	36154453	36154457	188950
181	Hapmap23854-BTC-062412	rs81154019	6	Production	36156604	36156608	187155
182	Hapmap23854-BTC-062412	rs81154019	6	Production	36156892	36156896	183350
183	Hapmap23854-BTC-062412	rs81154019	6	Production	36156892	36156896	187196
184	Hapmap23854-BTC-062412	rs81154019	6	Production	36157620	36157624	23700
185	Hapmap23854-BTC-062412	rs81154019	6	Production	36157620	36157624	66352
186	Hapmap23854-BTC-062412	rs81154019	6	Production	36157620	36157624	66353
187	Hapmap23854-BTC-062412	rs81154019	6	Production	36157620	36157624	66354
188	Hapmap23854-BTC-062412	rs81154019	6	Production	36157620	36157624	66355
189	Hapmap23854-BTC-062412	rs81154019	6	Production	36157620	36157624	67222
190	Hapmap23854-BTC-062412	rs81154019	6	Production	36157620	36157624	67223
191	Hapmap23854-BTC-062412	rs81154019	6	Production	36157620	36157624	164068
192	Hapmap23854-BTC-062412	rs81154019	6	Production	36157620	36157624	164167
193	Hapmap23854-BTC-062412	rs81154019	6	Meat_and_Carcass	36157620	36157624	164343
194	Hapmap23854-BTC-062412	rs81154019	6	Production	36157620	36157624	164369
195	Hapmap23854-BTC-062412	rs81154019	6	Production	36157620	36157624	164488
196	Hapmap23854-BTC-062412	rs81154019	6	Production	36157620	36157624	183351
197	Hapmap23854-BTC-062412	rs81154019	6	Production	36157620	36157624	186777
198	Hapmap23854-BTC-062412	rs81154019	6	Production	36157986	36157990	187308
199	Hapmap23854-BTC-062412	rs81154019	6	Production	36158359	36158363	187062
200	Hapmap23854-BTC-062412	rs81154019	6	Production	36158498	36158502	186952
201	Hapmap23854-BTC-062412	rs81154019	6	Production	36159606	36159610	183352
202	Hapmap23854-BTC-062412	rs81154019	6	Production	36159606	36159610	187046
203	Hapmap23854-BTC-062412	rs81154019	6	Production	36160694	36160698	187096
204	Hapmap23854-BTC-062412	rs81154019	6	Production	36161273	36161277	187575
205	Hapmap23854-BTC-062412	rs81154019	6	Production	36161375	36161379	186498
206	Hapmap23854-BTC-062412	rs81154019	6	Production	36161625	36161629	187580
207	Hapmap23854-BTC-062412	rs81154019	6	Production	36161633	36161637	187885
208	Hapmap23854-BTC-062412	rs81154019	6	Production	36162950	36162954	187283
209	Hapmap23854-BTC-062412	rs81154019	6	Production	36163340	36163344	187023
210	Hapmap23854-BTC-062412	rs81154019	6	Production	36163428	36163432	186547
211	Hapmap23854-BTC-062412	rs81154019	6	Reproduction	36163939	36163943	14703
212	Hapmap23854-BTC-062412	rs81154019	6	Reproduction	36163939	36163943	14707
213	Hapmap23854-BTC-062412	rs81154019	6	Production	36163939	36163943	189833
214	Hapmap23854-BTC-062412	rs81154019	6	Production	36164020	36164024	189834
215	Hapmap23854-BTC-062412	rs81154019	6	Production	36164564	36164568	187124
216	Hapmap23854-BTC-062412	rs81154019	6	Production	36165001	36165005	186965
217	Hapmap23854-BTC-062412	rs81154019	6	Production	36165137	36165141	187629
218	Hapmap23854-BTC-062412	rs81154019	6	Production	36165150	36165154	188219
219	Hapmap23854-BTC-062412	rs81154019	6	Production	36165218	36165222	186478
220	Hapmap23854-BTC-062412	rs81154019	6	Production	36165305	36165309	187300
221	Hapmap23854-BTC-062412	rs81154019	6	Production	36165645	36165649	186485
222	Hapmap23854-BTC-062412	rs81154019	6	Production	36166108	36166112	187795
223	Hapmap23854-BTC-062412	rs81154019	6	Production	36166178	36166182	187257
224	Hapmap23854-BTC-062412	rs81154019	6	Production	36166205	36166209	187129
225	Hapmap23854-BTC-062412	rs81154019	6	Production	36166298	36166302	187368
226	Hapmap23854-BTC-062412	rs81154019	6	Reproduction	36166503	36166507	14704
227	Hapmap23854-BTC-062412	rs81154019	6	Milk	36166503	36166507	105821
228	Hapmap23854-BTC-062412	rs81154019	6	Production	36166503	36166507	189279
229	Hapmap23854-BTC-062412	rs81154019	6	Production	36166791	36166795	189278
230	Hapmap23854-BTC-062412	rs81154019	6	Production	36166795	36166799	182735
231	Hapmap23854-BTC-062412	rs81154019	6	Production	36166795	36166799	189277
232	Hapmap23854-BTC-062412	rs81154019	6	Production	36167325	36167329	183190
233	Hapmap23854-BTC-062412	rs81154019	6	Production	36167325	36167329	186569
234	Hapmap23854-BTC-062412	rs81154019	6	Production	36167828	36167832	183191
235	Hapmap23854-BTC-062412	rs81154019	6	Production	36167828	36167832	188015
236	Hapmap23854-BTC-062412	rs81154019	6	Production	36168124	36168128	183305
237	Hapmap23854-BTC-062412	rs81154019	6	Production	36168124	36168128	186440
238	Hapmap23854-BTC-062412	rs81154019	6	Production	36168592	36168596	182749
239	Hapmap23854-BTC-062412	rs81154019	6	Production	36168592	36168596	186494
240	Hapmap23854-BTC-062412	rs81154019	6	Production	36168720	36168724	182760
241	Hapmap23854-BTC-062412	rs81154019	6	Production	36168720	36168724	186560
242	Hapmap23854-BTC-062412	rs81154019	6	Production	36169712	36169716	187059
243	Hapmap23854-BTC-062412	rs81154019	6	Production	36169792	36169796	186511
244	Hapmap23854-BTC-062412	rs81154019	6	Milk	36170113	36170117	140218
245	Hapmap23854-BTC-062412	rs81154019	6	Production	36170133	36170137	186638
246	Hapmap23854-BTC-062412	rs81154019	6	Production	36170199	36170203	187467
247	Hapmap23854-BTC-062412	rs81154019	6	Production	36171684	36171688	188463
248	Hapmap23854-BTC-062412	rs81154019	6	Production	36172370	36172374	187237
249	Hapmap23854-BTC-062412	rs81154019	6	Milk	36172376	36172380	246431
250	Hapmap23854-BTC-062412	rs81154019	6	Production	36177199	36177203	183152
251	Hapmap23854-BTC-062412	rs81154019	6	Production	36177199	36177203	188011
252	Hapmap23854-BTC-062412	rs81154019	6	Production	36180643	36180647	187727
253	Hapmap23854-BTC-062412	rs81154019	6	Production	36181019	36181023	187465
254	Hapmap23854-BTC-062412	rs81154019	6	Production	36182792	36182796	187002
255	Hapmap23854-BTC-062412	rs81154019	6	Production	36183911	36183915	186509
256	Hapmap23854-BTC-062412	rs81154019	6	Milk	36185332	36185336	250783
257	Hapmap23854-BTC-062412	rs81154019	6	Milk	36186103	36186107	103888
258	Hapmap23854-BTC-062412	rs81154019	6	Milk	36186103	36186107	104752
259	Hapmap23854-BTC-062412	rs81154019	6	Production	36186581	36186585	188641
260	Hapmap23854-BTC-062412	rs81154019	6	Production	36186688	36186692	189503
261	Hapmap23854-BTC-062412	rs81154019	6	Production	36187039	36187043	188875
262	Hapmap23854-BTC-062412	rs81154019	6	Production	36189076	36189080	188768
263	Hapmap23854-BTC-062412	rs81154019	6	Production	36191254	36191258	187381
264	Hapmap23854-BTC-062412	rs81154019	6	Production	36198700	36198704	186480
265	Hapmap23854-BTC-062412	rs81154019	6	Production	36203596	36203600	187794
266	Hapmap23854-BTC-062412	rs81154019	6	Milk	36204034	36204038	138157
267	Hapmap23854-BTC-062412	rs81154019	6	Production	36204034	36204038	186572
268	Hapmap23854-BTC-062412	rs81154019	6	Milk	36205214	36205218	15002
269	Hapmap23854-BTC-062412	rs81154019	6	Production	36206851	36206855	188861
270	Hapmap23854-BTC-062412	rs81154019	6	Production	36210548	36210552	187623
271	Hapmap23854-BTC-062412	rs81154019	6	Production	36211595	36211599	187645
272	Hapmap23854-BTC-062412	rs81154019	6	Production	36212184	36212188	189171
273	Hapmap23854-BTC-062412	rs81154019	6	Production	36212912	36212916	187151
274	Hapmap23854-BTC-062412	rs81154019	6	Production	36216422	36216426	187483
275	Hapmap23854-BTC-062412	rs81154019	6	Milk	36218000	36218004	103890
276	Hapmap23854-BTC-062412	rs81154019	6	Milk	36218000	36218004	104754
277	Hapmap23854-BTC-062412	rs81154019	6	Milk	36219842	36219846	105620
278	Hapmap23854-BTC-062412	rs81154019	6	Production	36219842	36219846	186455
279	Hapmap23854-BTC-062412	rs81154019	6	Production	36221082	36221086	187299
280	Hapmap23854-BTC-062412	rs81154019	6	Production	36221438	36221442	187378
281	Hapmap23854-BTC-062412	rs81154019	6	Production	36225616	36225620	186759
282	Hapmap23854-BTC-062412	rs81154019	6	Production	36226441	36226445	186605
283	Hapmap23854-BTC-062412	rs81154019	6	Production	36226963	36226967	23701
284	Hapmap23854-BTC-062412	rs81154019	6	Production	36226963	36226967	67245
285	Hapmap23854-BTC-062412	rs81154019	6	Production	36226963	36226967	67246
286	Hapmap23854-BTC-062412	rs81154019	6	Production	36226963	36226967	67247
287	Hapmap23854-BTC-062412	rs81154019	6	Milk	36226963	36226967	105509
288	Hapmap23854-BTC-062412	rs81154019	6	Production	36226963	36226967	164069
289	Hapmap23854-BTC-062412	rs81154019	6	Production	36226963	36226967	164370
290	Hapmap23854-BTC-062412	rs81154019	6	Production	36226963	36226967	186841
291	Hapmap23854-BTC-062412	rs81154019	6	Production	36227839	36227843	182676
292	Hapmap23854-BTC-062412	rs81154019	6	Production	36227839	36227843	188422
293	Hapmap23854-BTC-062412	rs81154019	6	Production	36232422	36232426	187967
294	Hapmap23854-BTC-062412	rs81154019	6	Production	36232742	36232746	187367
295	Hapmap23854-BTC-062412	rs81154019	6	Production	36233671	36233675	188839
296	Hapmap47669-BTA-59022	rs41645754	24	Exterior	857726	857730	66121
297	Hapmap47669-BTA-59022	rs41645754	24	Reproduction	885598	885602	106889

QTL type My Image

QTL name by type My Image

9.3.7.1 QTL enrichment on GALLO

#QTL enrichment analysis 
out.enrich_qtl_name <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "Name",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)


# Sorting the dataframe in ascending order of adj.pval
sorted_df <- out.enrich_qtl_name[order(out.enrich_qtl_name$adj.pval), ]

write.csv(sorted_df,"/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_enrich_qtl_genome_name.csv")

out.enrich_qtl_type <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "QTL_type",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)

sorted_df_type <- out.enrich_qtl_type[order(out.enrich_qtl_type$adj.pval), ]
write.csv(out.enrich_qtl_type,"/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_enrich_qtl_genome_type.csv")


#Plots

#Name

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_name$ID<-paste(out.enrich_qtl_name$QTL," - ","CHR",out.enrich_qtl_name$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered<-out.enrich_qtl_name[which(out.enrich_qtl_name$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered, x="ID", pval="adj.pval")


#Type

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_type$ID<-paste(out.enrich_qtl_type$QTL," - ","CHR",out.enrich_qtl_type$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered_type<-out.enrich_qtl_type[which(out.enrich_qtl_type$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered_type, x="ID", pval="adj.pval")

QTL Enrichment outcomes

Enrichment by name (enrichment analysis will be performed for each trait individually)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval	QTL_type
27	Metabolic body weight	83	4039	292	163224	0.0000000	0.0000000	Production
29	Milk butyric acid content	22	828	292	163224	0.0000000	0.0000000	Milk
30	Milk caproic acid content	18	675	292	163224	0.0000000	0.0000000	Milk
12	Dry matter intake	17	2186	292	163224	0.0000006	0.0000090	Production
2	Aggressive behavior	2	8	292	163224	0.0000887	0.0008719	Exterior
25	Length of productive life	13	2004	292	163224	0.0000815	0.0008719	Production
35	Milk pentadecylic acid content	5	280	292	163224	0.0001640	0.0013823	Milk
33	Milk iron content	4	217	292	163224	0.0006685	0.0049302	Milk
15	Dystocia	2	31	292	163224	0.0014331	0.0090411	Reproduction
16	Fecal larva count	3	125	292	163224	0.0015324	0.0090411	Health
4	Body weight	16	4289	292	163224	0.0050041	0.0268400	Production
14	Duration of inactivity during open field test	1	9	292	163224	0.0159862	0.0785990	Exterior
13	Duration of exploration during novel object test	1	16	292	163224	0.0282437	0.1281828	Exterior
5	Body weight gain	6	1354	292	163224	0.0362258	0.1526660	Production
3	Anti-Müllerian hormone level	1	26	292	163224	0.0454908	0.1789305	Health
46	Polyunsaturated fatty acid content	1	28	292	163224	0.0489034	0.1803314	Meat and Carcass
45	Palmitoleic acid content	1	46	292	163224	0.0790752	0.2744374	Meat and Carcass
42	Muscle sodium content	1	56	292	163224	0.0954231	0.3127758	Meat and Carcass
28	Milk alpha-S1-casein percentage	1	65	292	163224	0.1098888	0.3382449	Milk
43	Myristoleic acid content	1	68	292	163224	0.1146593	0.3382449	Meat and Carcass
39	Milk unglycosylated kappa-casein percentage	7	2351	292	163224	0.1315212	0.3695119	Milk
21	Interdigital hyperplasia	1	93	292	163224	0.1534352	0.4052354	Exterior
41	Multiple birth	1	96	292	163224	0.1579731	0.4052354	Reproduction
19	Gestation length	2	636	292	163224	0.3149170	0.7146195	Reproduction
23	Interval to first estrus after calving	3	1053	292	163224	0.2917668	0.7146195	Reproduction
36	Milk protein percentage	18	8803	292	163224	0.3139794	0.7146195	Milk
8	Calving ease	6	3819	292	163224	0.6804471	0.9778366	Reproduction
10	Conception rate	2	1255	292	163224	0.6577041	0.9778366	Reproduction
17	Feet and leg conformation	1	627	292	163224	0.6752950	0.9778366	Exterior
18	Foot angle	1	672	292	163224	0.7005282	0.9778366	Exterior
22	Interval from first to last insemination	1	445	292	163224	0.5497190	0.9778366	Reproduction
24	Lean meat yield	1	621	292	163224	0.6717742	0.9778366	Meat and Carcass
38	Milk riboflavin content	1	509	292	163224	0.5986072	0.9778366	Milk
48	PTA type	1	627	292	163224	0.6752950	0.9778366	Production
49	Rear leg placement - side view	1	430	292	163224	0.5374280	0.9778366	Exterior
50	Rump width	1	526	292	163224	0.6106790	0.9778366	Production
52	Somatic cell score	2	1122	292	163224	0.5971200	0.9778366	Health
53	Stillbirth	2	1363	292	163224	0.7013705	0.9778366	Reproduction
54	Strength	1	664	292	163224	0.6961897	0.9778366	Exterior
55	Subcutaneous fat thickness	1	331	292	163224	0.4474847	0.9778366	Meat and Carcass
57	Udder attachment	1	655	292	163224	0.6912339	0.9778366	Exterior
58	Udder depth	1	695	292	163224	0.7126605	0.9778366	Exterior
59	Udder height	1	504	292	163224	0.5949862	0.9778366	Exterior
6	Bovine respiratory disease susceptibility	1	789	292	163224	0.7573589	0.9935536	Health
20	Inseminations per conception	1	790	292	163224	0.7577951	0.9935536	Reproduction
1	Age at puberty	1	8222	292	163224	0.9999997	0.9999997	Reproduction
7	Bovine tuberculosis susceptibility	1	1155	292	163224	0.8744991	0.9999997	Health
9	Carcass weight	1	2020	292	163224	0.9737344	0.9999997	Meat and Carcass
11	Connective tissue amount	1	3142	292	163224	0.9965892	0.9999997	Meat and Carcass
26	Marbling score	1	1817	292	163224	0.9620577	0.9999997	Meat and Carcass
31	Milk fat percentage	9	10941	292	163224	0.9979006	0.9999997	Milk
32	Milk fat yield	2	8220	292	163224	0.9999954	0.9999997	Milk
34	Milk kappa-casein percentage	5	4499	292	163224	0.9064431	0.9999997	Milk
37	Milk protein yield	1	3093	292	163224	0.9962701	0.9999997	Milk
40	Milk yield	4	6432	292	163224	0.9970834	0.9999997	Milk
44	Net merit	1	903	292	163224	0.8023701	0.9999997	Production
47	Pregnancy rate	1	944	292	163224	0.8164356	0.9999997	Reproduction
51	Shear force	2	2954	292	163224	0.9692776	0.9999997	Meat and Carcass
56	Tenderness score	4	3483	292	163224	0.8712212	0.9999997	Meat and Carcass

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Enrichment by QTL_type (enrichment processes performed for the QTL classes)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval
1	Exterior	12	9077	292	163224	0.8914062	1
2	Health	8	5889	292	163224	0.8293292	1
3	Meat and Carcass	15	18258	292	163224	0.9998999	1
4	Milk	97	75352	292	163224	0.9999974	1
5	Production	138	19640	292	163224	0.0000000	0
6	Reproduction	22	35008	292	163224	1.0000000	1

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9.3.7.2 BLUPF90+ GPROFILER ON-LINE

From the online version of GPROFILER i got the following results.

Legend My Image

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9.3.7.3 BLUPF90+ VEP

My Image

rs110079750 My Image

rs109276211 My Image

rs109631116 My Image

rs42029843

rs110428837 My Image

rs110081798 My Image

rs110160157 My Image

rs110038841 My Image

rs109868537 My Image

rs110565206 My Image

rs42751504 My Image

rs110121846 My Image

rs110935391 My Image

rs109584097 My Image

rs41585925 My Image

rs110506037 My Image

rs109273103 My Image

rs109575643 My Image

rs43305418 My Image

rs43377276 My Image

rs43691687 My Image

rs41764450 My Image

rs42305073 No

rs42462935 No

rs42752353 My Image

rs81154019 No

rs41645754 My Image

rs41566051 My Image

rs41636137 My Image

rs41615935 My Image

rs29026778 My Image

rs29013980 My Image

rs29023486 My Image

rs29027498 My Image

rs29017061 My Image

rs29018741 My Image

rs29013301 My Image

rs29012492 My Image

It is interesting that 3 significant SNPs falled in the same gene CSMD2 on chromosome 3 rs109868537 rs42751504 rs42752353

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For the gene CNTNAP2 on chromosome 4 also have 2 significant SNPs rs41566051 rs29012492

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9.4 BLUPF90+ WINDOWS

9.4.1 Running renf90_ssGWAS2_SNPeff_w.par for GWAS (WINDOWS)

The parameter card bellow we are going to run using the software postGSf90:

renf90_ssGWAS2_SNPeff_W_10.par

# BLUPF90 parameter file created by RENUMF90
DATAFILE
renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        22 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   77182.    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   28212.    
OPTION SNP_file bruno_gntps_iid_71_clean
OPTION origID
OPTION no_quality_control
OPTION readGInverse
OPTION readA22Inverse
OPTION map_file snpmap.txt_clean
OPTION snp_p_value
OPTION Manhattan_plot_R
OPTION Manhattan_plot
OPTION SNP_moving_average 10
OPTION windows_variance 10

The parameter file above will generate the following files:

snp_sol
- column 1: trait
- column 2: effect
- column 3: SNP
- column 4: Chromosome
- column 5: Position
- column 6: SNP solution
- column 7: weight
- column 8: variance explained by n adjacents SNP (if OPTION windows_variance is used).
- column 9: variance of the SNP solution (used to compute the p-value) (if OPTION snp_p_value is used)
snp_pred
- contains allele frequencies + SNP effects
chrsnpvar
- column 1: trait
- column 2: effect
- column 3: variance explained by n adjacents SNP
- column 4: SNP
- column 5: Chromosome
- column 6: Position
chrsnp_pval
- column 1: trait
- column 2: effect
- column 3: -log10(p-value)
- column 4: SNP
- column 5: Chromosome
- column 6: Position in bp
chrsnp
- column 1: trait
- column 2: effect
- column 3: values of SNP effects to use in Manhattan plots → [abs(SNP_i)/SD(SNP)]
- column 4: SNP
- column 5: Chromosome
- column 6: Position
windows_segments
- column 1: label
- column 2: window size (number of SNP)
- column 3: Start SNP number for the window
- column 4: End SNP number for the window
- column 5: identification of window: (ChrNumber)’_’(startPositionMBP)
- column 6: Start (ChrNumber)’_’(Position) for the window
- column 7: End (ChrNumber)’_’(Position) for the window
windows_variance
- column 1: trait
- column 2: effect
- column 3: Start SNP number or SNP name for the window
- column 4: End SNP number or SNP name for the window
- column 5: window size (number of SNP)
- column 6: Start (ChrNumber)’_’(Position) for the window
- column 7: End (ChrNumber)’_’(Position) for the window
- column 8: identification of window: (ChrNumber)’_’(startPositionMBP)
- column 9: variance explained by n adjacents SNP
Vft1e3.R
Sft1e3.R
Pft1e3.R

Bellow we can see the SNPs that explain more than 0.5% of Genetic Variance

w_var <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/chrsnpvar", header = F)
w_var <- filter(w_var, V3 > 0.5)
colnames(w_var) <- c("V1", "V2", "Var", "SNP", "CHR", "BP")
snp_map <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/snpmap.txt_clean", header = T)

# Fazer o merge baseado em duas condições: CHR e POS
merged_data <- merge(w_var, snp_map, by.x = c("CHR", "BP"), by.y = c("CHR", "POS"), all.x = TRUE)
w_var <- merged_data[,c("CHR", "BP", "Var", "SNP_ID")]


rsid <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/SNPchimp_result_3859303481.tsv", header = T)


merged <- merge(rsid, w_var, by.x ="SNP_name", by.y ="SNP_ID")

colnames(merged)

merged <- merged[,c("SNP_name", "rs", "CHR", "BP", "Var")]

colnames(merged) <- c("SNP_name", "rsID", "CHR", "BP", "Var")

write.csv(merged, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w10_snp_rsid_snpvar_05.csv")

X	SNP_name	rsID	CHR	BP	Var
1	ARS-BFGL-BAC-28665	rs111010562	24	28487771	0.6899732
2	ARS-BFGL-BAC-35548	rs110100182	2	115665427	1.2851806
3	ARS-BFGL-BAC-7444	rs110491621	13	18558540	0.6630789
4	ARS-BFGL-NGS-103753	rs110842922	2	115821065	1.1689686
5	ARS-BFGL-NGS-107330	rs109766798	2	116018639	0.5958551
6	ARS-BFGL-NGS-25298	rs109868537	3	111772736	1.2101267
7	ARS-BFGL-NGS-2713	rs41761360	15	34054485	0.5549892
8	ARS-BFGL-NGS-30337	rs110485060	2	115730530	1.3488469
9	ARS-BFGL-NGS-3276	rs110634531	20	12087403	0.5722138
10	ARS-BFGL-NGS-37809	rs42751504	3	111751663	1.0607739
11	ARS-BFGL-NGS-43721	rs108974471	2	115986085	0.8178265
12	ARS-BFGL-NGS-44131	rs110100483	3	111806406	1.0252690
13	ARS-BFGL-NGS-5141	rs110705087	24	28417928	0.5945940
14	ARS-BFGL-NGS-5976	rs41763278	15	34144843	0.5490537
15	ARS-BFGL-NGS-6202	rs110385521	3	111833768	1.1671762
16	ARS-BFGL-NGS-78615	rs110959523	20	12111883	0.5689079
17	ARS-BFGL-NGS-85333	rs110742206	3	111933069	0.6077153
18	ARS-BFGL-NGS-97849	rs110553601	3	111965305	0.7547885
19	ARS-BFGL-NGS-98724	rs109709275	15	34109962	0.6252263
20	BTA-25900-no-rs	rs41575397	13	18509812	0.6364128
21	BTA-49096-no-rs	rs41578131	2	115695003	1.6071883
22	BTA-73915-no-rs	rs41648979	5	6312610	0.5103844
23	BTA-91816-no-rs	rs41596771	15	11100934	0.9739683
24	BTB-01421844	rs42544667	15	11144666	0.7131750
25	BTB-01421892	rs42544714	15	11185546	0.6463689
26	BTB-01421934	rs42545356	15	11207429	0.8898567
27	BTB-01422008	rs42545430	15	11236303	0.6832306
28	BTB-01434227	rs42557533	10	50554831	0.6660810
29	BTB-01485274	rs42609685	24	28540641	0.8983644
30	BTB-01608944	rs42723390	15	10974997	1.1206945
31	BTB-01641394	rs42752353	3	111730561	0.9480214
32	BTB-01646599	rs42761380	24	28604672	0.5894579
33	BTB-01813405	rs42924913	15	10879514	0.5556342
34	BTB-01830390	rs42938737	15	10936002	1.0480277
35	BTB-01948148	rs43056622	3	111677167	0.7467347
36	BTB-02063964	rs43172105	15	10906064	0.8788211
37	Hapmap41888-BTA-49091	rs41645223	2	115622067	0.8268392
38	Hapmap42062-BTA-109789	rs41621207	3	111708236	1.2415855
39	Hapmap49833-BTA-103929	rs41603335	13	18386942	0.5282867
40	Hapmap50266-BTA-13664	rs29018622	13	18266073	0.6006650
41	Hapmap54770-rs29009608	rs29009608	2	115875702	0.7398417
42	Hapmap54981-rs29019846	rs29019846	24	28516684	0.7747524
43	Hapmap58887-rs29013502	rs29013502	24	28570245	0.7619213

Bellow we can see the SNPs that explain more than 0.1% of Genetic Variance

w_var <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/chrsnpvar", header = F)
w_var <- filter(w_var, V3 > 0.1)
colnames(w_var) <- c("V1", "V2", "Var", "SNP", "CHR", "BP")
snp_map <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/snpmap.txt_clean", header = T)

# Fazer o merge baseado em duas condições: CHR e POS
merged_data <- merge(w_var, snp_map, by.x = c("CHR", "BP"), by.y = c("CHR", "POS"), all.x = TRUE)
w_var <- merged_data[,c("CHR", "BP", "SNP_ID", "Var")]


write.csv(w_var, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w10_snp_rsid_snpvar_01.csv")

ps: we don’t have rsID for the SNPs which explain more than 0.1% because there are 1,089 different SNPs and SNPchimp can’t deal with this ammount of data.

X	CHR	BP	SNP_ID	Var
1	1	101633865	BTB-00807137	0.1011903
2	1	101656030	BTB-00807095	0.2310848
3	1	101729648	BTA-51653-no-rs	0.1240284
4	1	101758062	BTB-00047321	0.1695202
5	1	101780022	ARS-BFGL-NGS-35839	0.1294318
6	1	101894473	ARS-BFGL-NGS-11628	0.1938700
7	1	101978904	BTA-46150-no-rs	0.1424979
8	1	102023440	ARS-BFGL-NGS-54096	0.1921858
9	1	102050836	ARS-BFGL-BAC-4585	0.1247982
10	1	103234850	BTB-00046247	0.1158039
11	1	103306203	BTB-02007023	0.1232947
12	1	103339600	Hapmap42893-BTA-27908	0.1289964
13	1	103405790	BTB-01831301	0.1173550
14	1	103442322	BTB-01579680	0.1228151
15	1	103465675	BTB-01579733	0.1250533
16	1	129991566	ARS-BFGL-NGS-29033	0.1062039
17	1	130055180	ARS-BFGL-NGS-44711	0.1817209
18	1	130171002	ARS-BFGL-NGS-6392	0.2399317
19	1	130204834	ARS-BFGL-NGS-56582	0.3492380
20	1	130225374	BTB-00058404	0.3332314
21	1	130269306	ARS-BFGL-NGS-117553	0.3155001
22	1	130333549	BTA-53036-no-rs	0.2752238
23	1	130363507	BTA-53040-no-rs	0.3226769
24	1	130404213	ARS-BFGL-BAC-5990	0.2582974
25	1	130441749	ARS-BFGL-NGS-111760	0.2461135
26	1	130473510	BTB-00058659	0.1743536
27	1	141713562	ARS-BFGL-NGS-114130	0.1027550
28	1	141793240	ARS-BFGL-NGS-31728	0.1038903
29	1	62768510	Hapmap49510-BTA-17452	0.1013706
30	1	7164618	BTA-44295-no-rs	0.1006185
31	1	7260253	BTA-45181-no-rs	0.1202808
32	1	93906113	BTB-01087150	0.1025507
33	1	93981570	Hapmap42095-BTA-120006	0.1232221
34	1	94035144	ARS-BFGL-NGS-14083	0.1086710
35	1	94088505	ARS-BFGL-NGS-106939	0.1432500
36	1	94134092	BTB-01086885	0.1064178
37	1	96716076	ARS-BFGL-NGS-4387	0.1049353
38	1	96747618	ARS-BFGL-NGS-96411	0.1863716
39	1	96820888	Hapmap42551-BTA-43327	0.1504778
40	1	96856897	ARS-BFGL-NGS-112806	0.1609532
41	1	96883533	BTA-43335-no-rs	0.1132002
42	1	96959413	ARS-BFGL-NGS-94323	0.1345075
43	1	97117901	BTA-43340-no-rs	0.1215449
44	10	14228843	ARS-BFGL-NGS-115059	0.1091091
45	10	14284093	ARS-BFGL-NGS-87702	0.1259792
46	10	15898865	ARS-BFGL-NGS-44234	0.1285237
47	10	15955819	ARS-BFGL-NGS-118236	0.2088687
48	10	15994647	Hapmap47826-BTA-107963	0.2763055
49	10	16137379	ARS-BFGL-BAC-14174	0.2618530
50	10	16160207	ARS-BFGL-NGS-5722	0.3713583
51	10	16182204	ARS-BFGL-NGS-113265	0.4067019
52	10	16211659	ARS-BFGL-NGS-16604	0.2890236
53	10	16346333	ARS-BFGL-NGS-87552	0.2102355
54	10	16434398	BTA-83414-no-rs	0.1754463
55	10	16454781	ARS-BFGL-NGS-64755	0.1610253
56	10	16482947	ARS-BFGL-NGS-43079	0.1010898
57	10	20629073	ARS-BFGL-NGS-33553	0.1191831
58	10	20746648	ARS-BFGL-NGS-57195	0.1243243
59	10	20776707	ARS-BFGL-BAC-10960	0.1315945
60	10	20803826	ARS-BFGL-NGS-42236	0.1818727
61	10	20834705	ARS-BFGL-NGS-44623	0.1702434
62	10	20860225	ARS-BFGL-NGS-110110	0.1283561
63	10	27185490	ARS-BFGL-BAC-11612	0.1467179
64	10	27219761	BTB-01402638	0.1192233
65	10	27257251	UA-IFASA-4465	0.1662910
66	10	27290407	ARS-BFGL-NGS-117948	0.1961370
67	10	27367801	BTB-01697310	0.1974927
68	10	27397646	Hapmap47512-BTA-114443	0.1828933
69	10	27420541	BTB-01402397	0.2176616
70	10	27547632	BTA-23031-no-rs	0.1080350
71	10	28233868	BTB-00417448	0.1652340
72	10	28382493	BTB-00414548	0.1388985
73	10	28417433	BTB-00414590	0.1441956
74	10	28450074	BTB-00414664	0.1264555
75	10	28490691	ARS-BFGL-NGS-3160	0.1201121
76	10	29764271	ARS-BFGL-NGS-46096	0.1067981
77	10	32278745	BTB-00416033	0.1549731
78	10	32303809	BTB-00416055	0.1030607
79	10	32324758	BTB-00416071	0.2002106
80	10	32400485	UA-IFASA-8860	0.1293646
81	10	32433557	BTB-00416188	0.1450198
82	10	32475837	BTB-00416290	0.1245701
83	10	32501730	BTB-00416386	0.1433081
84	10	32581893	BTA-62321-no-rs	0.1447698
85	10	41626921	BTA-96964-no-rs	0.1436904
86	10	41725845	BTA-67183-no-rs	0.1162669
87	10	41823523	Hapmap39501-BTA-67181	0.1439756
88	10	49767155	Hapmap43913-BTA-70188	0.1466788
89	10	49796175	ARS-BFGL-NGS-86147	0.1799150
90	10	49829868	BTB-00426818	0.1527360
91	10	49858694	ARS-BFGL-NGS-88023	0.1496056
92	10	49882556	ARS-BFGL-NGS-70374	0.1590110
93	10	50020863	BTB-00427118	0.1383846
94	10	50119457	BTB-00427217	0.1730071
95	10	50147936	BTB-00427152	0.1150314
96	10	50316929	BTA-70195-no-rs	0.1307258
97	10	50337554	BTB-02009817	0.2964468
98	10	50375243	Hapmap50870-BTA-111277	0.3896697
99	10	50392283	Hapmap41480-BTA-20737	0.2095723
100	10	50415615	BTB-01780583	0.1971266
101	10	50467891	ARS-BFGL-NGS-15394	0.2950876
102	10	50532545	BTA-92902-no-rs	0.4997714
103	10	50554831	BTB-01434227	0.6660810
104	10	50608000	Hapmap51989-BTA-92903	0.4718108
105	10	50632188	BTB-01434144	0.3879825
106	10	50673845	Hapmap54762-rs29012389	0.3575426
107	10	50727713	ARS-BFGL-NGS-8836	0.3454069
108	10	50804429	Hapmap45147-BTA-106533	0.1851935
109	10	50856455	ARS-BFGL-NGS-11399	0.2112604
110	10	50881434	ARS-BFGL-NGS-5470	0.1694916
111	10	50975312	BTB-00428084	0.1242463
112	10	80202103	BTB-00437473	0.1243366
113	10	80223687	ARS-BFGL-NGS-3980	0.2186830
114	10	80258289	UA-IFASA-4740	0.1468626
115	10	80402480	ARS-BFGL-NGS-59601	0.1736052
116	10	80433411	ARS-BFGL-BAC-11007	0.1234922
117	10	80464095	ARS-BFGL-NGS-6116	0.1566501
118	11	12952122	BTB-00460506	0.1061990
119	11	12975419	Hapmap48102-BTA-85468	0.1348135
120	11	13041787	Hapmap52124-rs29020943	0.1585891
121	11	13094818	ARS-BFGL-NGS-20431	0.1428201
122	11	13114842	ARS-BFGL-NGS-2493	0.1626706
123	11	13135356	ARS-BFGL-BAC-14209	0.1306841
124	11	16000971	UA-IFASA-2426	0.1052530
125	11	16110966	BTB-00462075	0.1259478
126	11	16135406	BTB-00461834	0.1063396
127	11	16174031	Hapmap29803-BTA-126260	0.1156433
128	11	16210947	BTB-00461875	0.1042706
129	11	16242950	BTB-00461926	0.1030392
130	11	25467022	ARS-BFGL-NGS-29851	0.1278897
131	11	25630059	BTB-00469321	0.1106776
132	11	25660041	Hapmap42357-BTA-89853	0.1031512
133	11	25760288	ARS-BFGL-NGS-5488	0.1083397
134	11	27648326	ARS-BFGL-NGS-24935	0.1028696
135	11	27678911	BTB-00470332	0.1348225
136	11	27709498	Hapmap58395-rs29019857	0.1645872
137	11	27746425	ARS-BFGL-NGS-101857	0.1297567
138	11	27830023	Hapmap48940-BTA-90883	0.1481492
139	11	28054041	ARS-BFGL-NGS-82906	0.1358081
140	11	29333199	ARS-BFGL-NGS-103581	0.1181737
141	11	29365973	ARS-BFGL-NGS-58445	0.1288754
142	11	29389110	ARS-BFGL-NGS-114530	0.1088233
143	11	29415659	ARS-BFGL-NGS-112640	0.1255939
144	11	29454559	ARS-BFGL-NGS-14040	0.1267679
145	11	29483268	ARS-BFGL-NGS-14449	0.1393408
146	11	29523821	ARS-BFGL-NGS-100266	0.1150293
147	11	58356755	BTA-34561-no-rs	0.1086553
148	11	58383311	BTA-33625-no-rs	0.1036073
149	11	58497959	ARS-BFGL-BAC-11747	0.1017918
150	11	83985738	ARS-BFGL-BAC-7319	0.1051094
151	11	84678345	Hapmap40930-BTA-26841	0.1016702
152	11	84738147	Hapmap25343-BTA-163074	0.1223724
153	11	84820376	BTA-26840-no-rs	0.1085350
154	11	85247389	ARS-BFGL-NGS-37939	0.1346684
155	11	85302066	ARS-BFGL-NGS-107825	0.1411083
156	11	85322845	Hapmap43168-BTA-119307	0.2008925
157	11	85359517	ARS-BFGL-NGS-40301	0.3055185
158	11	85398026	ARS-BFGL-NGS-110235	0.2711184
159	11	85606528	Hapmap57048-rs29017217	0.2491385
160	11	85771451	ARS-BFGL-NGS-111563	0.3654883
161	11	85810220	Hapmap39304-BTA-109342	0.2888586
162	11	85937536	Hapmap38876-BTA-109334	0.3037616
163	11	85972950	BTA-120876-no-rs	0.1692578
164	11	86081075	ARS-BFGL-NGS-14236	0.1476002
165	11	87104511	ARS-BFGL-BAC-7337	0.1237828
166	11	87194574	ARS-BFGL-NGS-38084	0.1577790
167	11	87223121	Hapmap52090-rs29022201	0.1741682
168	11	87279961	BTA-110370-no-rs	0.1707878
169	11	87301374	ARS-BFGL-NGS-114578	0.2879943
170	11	87343394	ARS-BFGL-NGS-115698	0.1751316
171	11	87427230	ARS-BFGL-NGS-89483	0.1791455
172	11	87450947	ARS-BFGL-NGS-77445	0.1715604
173	11	87477968	ARS-BFGL-NGS-102660	0.1509007
174	11	9212734	Hapmap40102-BTA-110410	0.1001916
175	11	9303545	ARS-BFGL-NGS-57976	0.1390568
176	11	9345296	ARS-BFGL-NGS-83673	0.1092555
177	11	95837864	Hapmap24798-BTA-127049	0.1160667
178	11	95985524	Hapmap40206-BTA-120879	0.2015491
179	11	96024175	ARS-BFGL-NGS-72642	0.2154334
180	11	96074398	Hapmap47514-BTA-115564	0.2903548
181	11	96235663	ARS-BFGL-NGS-101698	0.3932618
182	11	96285921	ARS-BFGL-NGS-111672	0.3447816
183	11	96322228	ARS-BFGL-NGS-43490	0.3858722
184	11	96454896	Hapmap60189-rs29017117	0.2919202
185	11	96479338	ARS-BFGL-NGS-50500	0.2485149
186	11	96609577	Hapmap51470-BTA-57893	0.1719578
187	11	96699491	Hapmap51471-BTA-57896	0.1120564
188	12	10779144	BTB-01100174	0.1653103
189	12	10830532	Hapmap35046-BES11_Contig381_736	0.2531744
190	12	10851132	ARS-BFGL-NGS-21526	0.2305728
191	12	10967287	Hapmap48641-BTA-119400	0.2371592
192	12	11150596	ARS-BFGL-NGS-43211	0.3360266
193	12	11193032	ARS-BFGL-NGS-87459	0.2200374
194	12	11324144	ARS-BFGL-NGS-71187	0.1566248
195	12	11385069	BTB-00486553	0.1306997
196	12	11414286	BTB-00487004	0.1364686
197	12	14005588	BTB-01296488	0.1154279
198	12	14056621	Hapmap49074-BTA-19146	0.1209334
199	12	14083373	BTB-01475864	0.1482084
200	12	14109902	BTB-01296573	0.1204048
201	12	14156556	ARS-BFGL-NGS-6072	0.1644307
202	12	14186758	BTB-01296818	0.1135065
203	12	56021401	Hapmap51019-BTA-65454	0.1058319
204	12	56034163	ARS-BFGL-NGS-104509	0.1206410
205	12	56206814	BTB-01834845	0.1078660
206	12	56276159	Hapmap43219-BTA-26954	0.1122969
207	13	17967955	Hapmap51587-BTA-34220	0.1319741
208	13	18001601	ARS-BFGL-NGS-31365	0.1763165
209	13	18167455	Hapmap39556-BTA-34210	0.2841578
210	13	18189413	ARS-BFGL-NGS-30447	0.4135777
211	13	18266073	Hapmap50266-BTA-13664	0.6006650
212	13	18386942	Hapmap49833-BTA-103929	0.5282867
213	13	18489670	ARS-BFGL-NGS-21967	0.4937232
214	13	18509812	BTA-25900-no-rs	0.6364128
215	13	18558540	ARS-BFGL-BAC-7444	0.6630789
216	13	18626070	ARS-BFGL-NGS-116274	0.4947973
217	13	18676544	BTA-31832-no-rs	0.3995710
218	13	18720587	Hapmap43233-BTA-31838	0.2473578
219	13	18755905	BTA-10553-rs29016106	0.1566573
220	13	18792716	ARS-BFGL-NGS-72609	0.1333990
221	13	18822522	ARS-BFGL-NGS-13518	0.1014681
222	13	18870958	ARS-BFGL-NGS-68568	0.1334652
223	13	18892498	ARS-BFGL-NGS-109707	0.1530972
224	13	23248667	Hapmap39219-BTA-31890	0.1386580
225	13	23543854	ARS-BFGL-NGS-112203	0.1473551
226	13	23590146	ARS-BFGL-NGS-101509	0.1353489
227	13	23647519	ARS-BFGL-NGS-99560	0.1323049
228	13	23807096	ARS-BFGL-NGS-80617	0.1505820
229	13	23896954	ARS-BFGL-NGS-14188	0.1365098
230	13	23925448	ARS-BFGL-NGS-91120	0.1240624
231	13	23993075	ARS-BFGL-NGS-31326	0.1228236
232	13	26101077	Hapmap39018-BTA-31978	0.1238798
233	13	58794363	ARS-BFGL-BAC-13190	0.1098366
234	13	59661039	BTB-00534445	0.1336180
235	13	59704623	Hapmap50319-BTA-33109	0.1403262
236	13	59728737	ARS-BFGL-NGS-103807	0.1193523
237	13	59762662	BTB-00534589	0.1499035
238	13	59828052	ARS-BFGL-NGS-108965	0.1342105
239	13	59901998	Hapmap50919-BTA-23085	0.1171896
240	13	59930801	ARS-BFGL-NGS-35771	0.1002085
241	13	75354084	ARS-BFGL-NGS-31974	0.1064442
242	14	12249541	Hapmap23784-BTC-010226	0.1417963
243	14	12811226	UA-IFASA-7429	0.1023518
244	14	12832792	Hapmap56398-rs29010937	0.1966127
245	14	12882729	UA-IFASA-7696	0.3361687
246	14	12970779	Hapmap41845-BTA-35927	0.3373003
247	14	13043711	Hapmap47521-BTA-120525	0.2765382
248	14	13104401	UA-IFASA-8537	0.2641441
249	14	13357962	ARS-BFGL-NGS-104239	0.2672164
250	14	13400490	ARS-BFGL-NGS-35935	0.2016124
251	14	13975863	ARS-BFGL-NGS-19893	0.2394582
252	14	14008185	Hapmap58424-rs29021188	0.2315530
253	14	14041208	BTB-01109852	0.1868193
254	14	14072969	BTB-01109980	0.1048374
255	14	15929822	ARS-BFGL-NGS-82859	0.1135950
256	14	15956824	ARS-BFGL-BAC-10591	0.2270443
257	14	15998933	ARS-BFGL-BAC-14014	0.2031215
258	14	16025038	Hapmap38314-BTA-42171	0.2014397
259	14	16083548	Hapmap60650-rs29021886	0.2162313
260	14	16161546	Hapmap47609-BTA-42173	0.1977502
261	14	16242981	ARS-BFGL-NGS-30433	0.1730677
262	14	16283570	BTA-35990-no-rs	0.1514607
263	14	16409637	BTB-00553442	0.1669021
264	14	19339380	ARS-BFGL-NGS-102399	0.1488057
265	14	25923373	ARS-BFGL-BAC-1302	0.1040812
266	14	64678934	ARS-BFGL-NGS-28894	0.1167324
267	14	64708627	Hapmap49582-BTA-35282	0.2010022
268	14	64752348	Hapmap39171-BTA-35277	0.2158376
269	14	64848878	UA-IFASA-5830	0.1187073
270	14	64929189	ARS-BFGL-BAC-24806	0.1224654
271	14	65078173	ARS-BFGL-NGS-16512	0.1095411
272	14	65243234	ARS-BFGL-NGS-24670	0.1328391
273	14	65391553	ARS-BFGL-NGS-115837	0.1223244
274	14	65761220	ARS-BFGL-NGS-7028	0.1026649
275	14	65824361	UA-IFASA-8451	0.1099946
276	14	66158833	BTA-07375-no-rs	0.1514500
277	14	66258598	ARS-BFGL-NGS-11838	0.1136638
278	14	66308321	ARS-BFGL-NGS-33213	0.1342321
279	14	66350939	ARS-BFGL-NGS-59595	0.1038174
280	14	66525315	ARS-BFGL-NGS-3386	0.1005279
281	14	69322493	ARS-BFGL-NGS-10628	0.1153971
282	14	69343258	Hapmap42350-BTA-88359	0.1058892
283	14	70964025	UA-IFASA-7523	0.1075925
284	15	10740697	BTB-01538650	0.1416967
285	15	10827424	ARS-BFGL-BAC-2191	0.3020577
286	15	10879514	BTB-01813405	0.5556342
287	15	10906064	BTB-02063964	0.8788211
288	15	10936002	BTB-01830390	1.0480277
289	15	10974997	BTB-01608944	1.1206945
290	15	11100934	BTA-91816-no-rs	0.9739683
291	15	11144666	BTB-01421844	0.7131750
292	15	11185546	BTB-01421892	0.6463689
293	15	11207429	BTB-01421934	0.8898567
294	15	11236303	BTB-01422008	0.6832306
295	15	11310260	BTA-91820-no-rs	0.4420277
296	15	11451408	Hapmap40037-BTA-100798	0.2263849
297	15	11816949	ARS-BFGL-NGS-4520	0.1303515
298	15	33804402	Hapmap57840-rs29014510	0.1571340
299	15	33956411	BTA-36425-no-rs	0.2054084
300	15	34001596	ARS-BFGL-NGS-26086	0.3355725
301	15	34025604	ARS-BFGL-NGS-75576	0.4695660
302	15	34054485	ARS-BFGL-NGS-2713	0.5549892
303	15	34109962	ARS-BFGL-NGS-98724	0.6252263
304	15	34144843	ARS-BFGL-NGS-5976	0.5490537
305	15	34205212	ARS-BFGL-NGS-111085	0.4880066
306	15	34225443	ARS-BFGL-NGS-116109	0.4224669
307	15	34249244	BTB-00593773	0.3560422
308	15	34273013	UA-IFASA-8490	0.2780633
309	15	34314815	ARS-BFGL-NGS-106696	0.2158683
310	15	50354219	Hapmap61092-rs29026735	0.1068255
311	15	57629970	ARS-BFGL-BAC-6489	0.1038292
312	15	61810673	BTB-01230622	0.1459620
313	15	61855683	Hapmap49365-BTA-89419	0.1193815
314	15	61914231	Hapmap58607-rs29013975	0.1187790
315	15	62058172	BTB-01406897	0.1681714
316	15	62235431	BTB-01788867	0.1049774
317	15	73647677	BTB-00479280	0.1295441
318	15	73704148	BTB-00479196	0.1328067
319	15	73744665	BTB-00479177	0.1866316
320	15	73774929	Hapmap46540-BTA-93429	0.1336160
321	15	73932618	ARS-BFGL-NGS-113046	0.2194570
322	15	73964566	ARS-BFGL-NGS-44270	0.1487461
323	15	73991541	BTB-01206179	0.1383929
324	15	74019876	Hapmap54269-rs29023420	0.1177362
325	15	9385293	BTA-91114-no-rs	0.1005343
326	15	9454892	Hapmap50818-BTA-91110	0.1375597
327	16	11214992	BTB-01698122	0.1125842
328	16	11410055	ARS-BFGL-NGS-14292	0.1080565
329	16	1189722	ARS-BFGL-NGS-6005	0.1208638
330	16	1209875	ARS-BFGL-NGS-108427	0.1175028
331	16	1350556	ARS-BFGL-NGS-110989	0.1136302
332	16	1395952	Hapmap52241-rs29014076	0.1172834
333	16	1432322	Hapmap40972-BTA-38862	0.1541974
334	16	1456618	ARS-BFGL-NGS-21417	0.1459286
335	16	1480344	Hapmap35625-SCAFFOLD312099_6800	0.1450504
336	16	244385	ARS-BFGL-NGS-66185	0.1117863
337	16	34337719	Hapmap50308-BTA-29728	0.1011962
338	16	34451175	ARS-BFGL-NGS-100932	0.1180084
339	16	34493084	BTB-00638221	0.1762850
340	16	34537769	Hapmap46698-BTA-38760	0.1169510
341	16	53407745	BTA-39215-no-rs	0.1039673
342	16	53433033	ARS-BFGL-NGS-29043	0.1017457
343	16	53470013	BTB-00647119	0.1007320
344	16	53504840	Hapmap58300-rs29026453	0.1828180
345	16	53531283	ARS-BFGL-NGS-84228	0.1281363
346	16	5680582	BTB-02055130	0.1669712
347	16	60525984	BTB-01732320	0.1012978
348	16	60560617	Hapmap56001-rs29023690	0.1001974
349	16	60638392	BTB-00653779	0.1083641
350	16	60744478	BTB-00653808	0.1090935
351	16	60784885	ARS-BFGL-NGS-11469	0.1006348
352	16	60966792	ARS-BFGL-NGS-12581	0.1017713
353	16	6121692	BTA-95363-no-rs	0.2978286
354	16	6308221	BTB-01975868	0.2425433
355	16	6329170	ARS-BFGL-NGS-89740	0.1634005
356	16	6421207	BTB-01200020	0.1018885
357	16	6446024	BTB-01200008	0.1167632
358	16	719450	BTB-00623459	0.1010217
359	16	72000010	Hapmap55487-rs29023215	0.1122668
360	16	72025137	ARS-BFGL-NGS-106233	0.1440657
361	16	72146211	Hapmap51565-BTA-122868	0.1219199
362	17	28637922	BTB-00676077	0.1275495
363	17	28709448	Hapmap39420-BTA-65875	0.1309016
364	17	28946786	ARS-BFGL-NGS-30992	0.1211300
365	17	29056641	ARS-BFGL-BAC-32712	0.1043920
366	17	47697987	Hapmap38389-BTA-17091	0.1002335
367	17	52610220	ARS-BFGL-NGS-16431	0.1131629
368	17	5529898	ARS-BFGL-NGS-24849	0.1028224
369	17	60781502	ARS-BFGL-NGS-39993	0.1158658
370	17	63638557	ARS-BFGL-NGS-102231	0.1164977
371	17	63670448	ARS-BFGL-NGS-115604	0.1316881
372	17	63702565	BTB-00683557	0.1789678
373	17	63734585	BTB-00683408	0.2345361
374	17	63801977	ARS-BFGL-NGS-68160	0.2420047
375	17	63839661	ARS-BFGL-NGS-111681	0.1739346
376	17	63860049	ARS-BFGL-NGS-94330	0.1219070
377	17	63950062	ARS-BFGL-BAC-36611	0.1076534
378	17	63980425	ARS-BFGL-NGS-17192	0.1191434
379	17	64725259	ARS-BFGL-NGS-119559	0.1212831
380	17	64880442	BTA-41804-no-rs	0.1301090
381	17	64908699	BTB-00686787	0.1236264
382	17	64941889	ARS-BFGL-NGS-111098	0.1352275
383	17	64965089	Hapmap51227-BTA-41809	0.1595287
384	17	64994584	BTB-00686912	0.1197443
385	17	65019617	ARS-BFGL-NGS-41791	0.1025183
386	17	72650868	ARS-BFGL-NGS-102576	0.1390294
387	17	72708941	ARS-BFGL-NGS-117289	0.1341702
388	17	72733715	ARS-BFGL-NGS-61354	0.1105093
389	17	72754526	ARS-BFGL-NGS-16630	0.1094185
390	17	72912770	ARS-BFGL-NGS-108169	0.1231619
391	18	21231877	BTB-00705849	0.1286615
392	18	21337120	ARS-BFGL-NGS-111533	0.2542432
393	18	21391728	Hapmap46385-BTA-90833	0.4494537
394	18	21429894	Hapmap35049-BES5_Contig425_820	0.3313303
395	18	21454669	Hapmap42359-BTA-90829	0.3210384
396	18	21483055	ARS-BFGL-NGS-106934	0.2473642
397	18	21505378	Hapmap49904-BTA-19486	0.1525068
398	18	21555192	ARS-BFGL-NGS-43902	0.1039042
399	18	21791294	Hapmap42547-BTA-42724	0.1085655
400	18	26996637	ARS-BFGL-NGS-12130	0.1067412
401	18	27034659	BTA-19724-no-rs	0.1498781
402	18	27057109	Hapmap52207-rs29021316	0.1730526
403	18	27147855	ARS-BFGL-BAC-34194	0.1239610
404	18	27193722	Hapmap23161-BTA-162019	0.1545777
405	18	27285021	ARS-BFGL-NGS-29803	0.1820013
406	18	27360956	Hapmap50199-BTA-106275	0.1285033
407	18	27440587	Hapmap33051-BTA-154275	0.1660330
408	18	27466718	ARS-BFGL-BAC-34193	0.1800524
409	18	27489227	ARS-BFGL-NGS-5283	0.2332037
410	18	27556941	BTA-106270-no-rs	0.1855007
411	18	27873728	ARS-BFGL-NGS-36647	0.2357321
412	18	27938774	ARS-BFGL-NGS-101461	0.3809178
413	18	28048779	ARS-BFGL-NGS-110709	0.2835508
414	18	28122450	ARS-BFGL-NGS-59857	0.2280347
415	18	28195018	ARS-BFGL-NGS-73644	0.2370789
416	18	28233472	ARS-BFGL-NGS-108426	0.1694792
417	18	28333748	BTA-29270-no-rs	0.1150091
418	18	28989050	Hapmap49026-BTA-113661	0.1016615
419	18	29138185	Hapmap38811-BTA-113622	0.1060298
420	18	29200440	Hapmap45469-BTA-113627	0.1638232
421	18	29271888	Hapmap51157-BTA-116869	0.2002796
422	18	29298380	BTA-113636-no-rs	0.1764313
423	18	29324758	ARS-BFGL-NGS-106535	0.2624606
424	18	29364850	Hapmap55151-rs29025839	0.2795316
425	18	29467609	Hapmap48531-BTA-95258	0.2227679
426	18	29524292	ARS-BFGL-NGS-16967	0.1795746
427	18	29576686	Hapmap53786-rs29014011	0.1416327
428	18	29605571	Hapmap36782-SCAFFOLD167149_2583	0.1713824
429	18	29649247	BTA-95254-no-rs	0.1448184
430	18	54117753	ARS-BFGL-NGS-52477	0.1917897
431	18	54201723	Hapmap47295-BTA-43799	0.1907683
432	18	54265768	ARS-BFGL-NGS-108618	0.1963253
433	18	54423038	BTB-00726211	0.1866930
434	18	54524864	Hapmap38225-BTA-43804	0.1659251
435	18	54546398	ARS-BFGL-NGS-82046	0.1840717
436	18	54586408	ARS-BFGL-NGS-10623	0.1417040
437	18	54783476	ARS-BFGL-NGS-70161	0.1305656
438	18	6140925	BTA-121111-no-rs	0.1096684
439	18	6572449	ARS-BFGL-NGS-58546	0.1126790
440	18	6627295	ARS-BFGL-NGS-101349	0.1290278
441	18	6661805	ARS-BFGL-NGS-2504	0.1263538
442	18	6723849	BTA-03029-rs29010796	0.1317185
443	18	6747828	Hapmap54020-rs29023153	0.1609004
444	18	6788121	Hapmap23072-BTA-131895	0.1600989
445	18	6830147	ARS-BFGL-NGS-31035	0.1714779
446	18	6864571	BTA-42870-no-rs	0.1252620
447	19	41653976	ARS-BFGL-NGS-111199	0.1443223
448	19	41688325	UA-IFASA-9304	0.1724620
449	19	41758457	ARS-BFGL-NGS-38784	0.1778979
450	19	41788040	ARS-BFGL-NGS-111247	0.1404885
451	19	41836105	ARS-BFGL-NGS-1032	0.1390584
452	19	41922085	ARS-BFGL-NGS-106239	0.1311788
453	19	45438908	UA-IFASA-6117	0.1030924
454	19	45480873	ARS-BFGL-NGS-114067	0.1491889
455	19	45565481	BTA-45655-no-rs	0.1362124
456	19	45640308	ARS-BFGL-NGS-110481	0.1295812
457	19	45703318	ARS-BFGL-NGS-102317	0.1276231
458	19	45756376	UA-IFASA-8333	0.1275049
459	19	45809961	UA-IFASA-8910	0.1296455
460	19	45912712	ARS-BFGL-NGS-19397	0.1136813
461	19	45962318	ARS-BFGL-NGS-39129	0.1028512
462	19	50718075	ARS-BFGL-NGS-39328	0.1216000
463	19	50812576	ARS-BFGL-NGS-73980	0.1131424
464	19	51407028	ARS-BFGL-BAC-35684	0.1057903
465	19	51427990	ARS-BFGL-NGS-22719	0.1229333
466	19	51489174	ARS-BFGL-NGS-111298	0.1330036
467	19	51532636	ARS-BFGL-NGS-68655	0.1185929
468	19	51584353	ARS-BFGL-NGS-44098	0.1392985
469	19	51614996	ARS-BFGL-NGS-77313	0.1630706
470	19	51642943	ARS-BFGL-NGS-105532	0.1573968
471	19	51674934	ARS-BFGL-NGS-117951	0.1292450
472	19	51713082	Hapmap39750-BTA-45775	0.1099800
473	19	51772007	Hapmap42219-BTA-45770	0.1123407
474	19	51860856	ARS-BFGL-NGS-112589	0.1037553
475	19	51892710	BTB-00760167	0.1230534
476	2	115397512	Hapmap52537-rs29011369	0.1776675
477	2	115448114	BTB-00113596	0.1077871
478	2	115480079	ARS-BFGL-NGS-94004	0.1579225
479	2	115504029	BTB-00113625	0.3271297
480	2	115533946	Hapmap40249-BTA-49086	0.4998173
481	2	115622067	Hapmap41888-BTA-49091	0.8268392
482	2	115665427	ARS-BFGL-BAC-35548	1.2851806
483	2	115695003	BTA-49096-no-rs	1.6071883
484	2	115730530	ARS-BFGL-NGS-30337	1.3488469
485	2	115821065	ARS-BFGL-NGS-103753	1.1689686
486	2	115875702	Hapmap54770-rs29009608	0.7398417
487	2	115986085	ARS-BFGL-NGS-43721	0.8178265
488	2	116018639	ARS-BFGL-NGS-107330	0.5958551
489	2	116064090	Hapmap38323-BTA-49132	0.3476804
490	2	116123392	ARS-BFGL-NGS-107909	0.1830611
491	2	116186918	BTA-103078-no-rs	0.1022528
492	2	116211153	ARS-BFGL-NGS-112195	0.2646628
493	2	116340519	Hapmap44637-BTA-17098	0.1336603
494	2	118732813	ARS-BFGL-NGS-8841	0.1151775
495	2	118785254	ARS-BFGL-NGS-15508	0.1186105
496	2	22568193	Hapmap33359-BTA-133718	0.1539524
497	2	22608474	BTB-00084343	0.1096842
498	2	53401974	BTB-01412526	0.1062297
499	2	54862093	BTB-01294125	0.1539007
500	2	54912507	BTB-01293919	0.2862742
501	2	54937229	Hapmap51787-BTA-101971	0.2887689
502	2	54993038	Hapmap43438-BTA-101967	0.1804141
503	2	55019715	Hapmap38718-BTA-101966	0.2794705
504	2	55076037	BTB-01165311	0.2354211
505	2	55101502	ARS-BFGL-NGS-111213	0.1681381
506	2	55133902	Hapmap43745-BTA-101961	0.1759904
507	2	55164987	BTB-01165248	0.1514017
508	2	55339988	BTB-01165150	0.1349742
509	2	55360037	BTB-01892981	0.2520212
510	2	55394810	Hapmap23257-BTA-123353	0.4080735
511	2	55441390	Hapmap48508-BTA-88108	0.3278748
512	2	55510237	BTB-01753605	0.3562713
513	2	55577762	Hapmap33880-BES9_Contig535_933	0.3108049
514	2	55613073	Hapmap38699-BTA-81626	0.2747132
515	2	55633701	BTB-00353784	0.2962051
516	2	55657824	Hapmap44140-BTA-81617	0.2810288
517	2	55687114	BTB-00353647	0.2030612
518	2	55708250	BTB-01160731	0.1385554
519	2	64731462	UA-IFASA-3968	0.1101644
520	2	75066200	Hapmap51098-BTA-96589	0.2126561
521	2	75132529	Hapmap50173-BTA-96591	0.3371572
522	2	75307501	ARS-BFGL-NGS-32559	0.2367220
523	2	75466886	BTA-96598-no-rs	0.2985397
524	2	75615637	BTA-96609-no-rs	0.3299858
525	2	75669042	Hapmap24002-BTA-150995	0.2955836
526	2	75692826	BTA-96614-no-rs	0.3503897
527	2	75716491	ARS-BFGL-NGS-69160	0.2350321
528	2	75794795	Hapmap60737-rs29018861	0.1850975
529	2	75850641	ARS-BFGL-NGS-104820	0.1879055
530	20	11873589	BTB-01947189	0.1450606
531	20	11948110	ARS-BFGL-NGS-84611	0.1566419
532	20	12001911	ARS-BFGL-NGS-112994	0.2323316
533	20	12040873	Hapmap52341-rs29025776	0.4138388
534	20	12087403	ARS-BFGL-NGS-3276	0.5722138
535	20	12111883	ARS-BFGL-NGS-78615	0.5689079
536	20	12136194	Hapmap44603-BTA-51543	0.4286374
537	20	12166596	ARS-BFGL-BAC-31736	0.3700647
538	20	12228546	BTA-118245-no-rs	0.3054249
539	20	12265622	Hapmap42086-BTA-118249	0.2070786
540	20	12308594	ARS-BFGL-NGS-26205	0.1257296
541	20	12336676	BTA-51542-no-rs	0.1241793
542	20	45053767	ARS-BFGL-BAC-27247	0.1166841
543	20	45076553	Hapmap61037-rs29016327	0.1040639
544	20	45326625	BTB-01942525	0.1024135
545	20	45641953	Hapmap51381-BTA-107547	0.1229982
546	20	45677327	ARS-BFGL-NGS-37203	0.1969815
547	20	45726823	Hapmap32803-BTA-135212	0.1352632
548	21	44216692	BTB-00820947	0.1033610
549	21	66581100	ARS-BFGL-NGS-103918	0.1377335
550	22	19093982	BTB-00839323	0.1114517
551	22	19403914	ARS-BFGL-NGS-114883	0.1159227
552	22	35656826	ARS-BFGL-NGS-21808	0.1222127
553	22	35692840	BTA-54207-no-rs	0.1114537
554	22	35729340	Hapmap41295-BTA-54203	0.1117493
555	22	35933227	Hapmap50888-BTA-121350	0.1356556
556	22	36221354	BTA-22352-no-rs	0.1307752
557	22	36335811	BTA-27368-no-rs	0.2031229
558	22	36411261	Hapmap35530-SCAFFOLD65737_1657	0.2958015
559	22	36432552	ARS-BFGL-BAC-35732	0.3176352
560	22	36505459	ARS-BFGL-NGS-43172	0.2965640
561	22	36606947	ARS-BFGL-NGS-105840	0.2257325
562	22	38674217	ARS-BFGL-NGS-82909	0.1159211
563	22	38735749	Hapmap47039-BTA-54432	0.1011432
564	22	38760668	BTA-54431-no-rs	0.1052700
565	22	38832195	BTB-00846328	0.1025065
566	22	38933225	BTB-00846254	0.1055622
567	22	39042744	BTB-00846141	0.1241577
568	22	39080164	ARS-BFGL-NGS-55340	0.1337055
569	22	39130220	ARS-BFGL-NGS-71810	0.1624893
570	22	44563359	ARS-BFGL-NGS-81286	0.1175573
571	22	44704423	Hapmap42246-BTA-54605	0.1263321
572	22	49442636	ARS-BFGL-NGS-31703	0.1085793
573	22	49480862	ARS-BFGL-NGS-88154	0.1780732
574	22	49768282	ARS-BFGL-NGS-16330	0.1819542
575	22	49836856	ARS-BFGL-NGS-15965	0.2175102
576	22	49904097	ARS-BFGL-NGS-39459	0.1222554
577	22	49963223	ARS-BFGL-NGS-43179	0.1985014
578	22	49991511	ARS-BFGL-NGS-28703	0.1535508
579	22	50038809	ARS-BFGL-NGS-96011	0.1343603
580	22	50081546	Hapmap60583-rs29027190	0.1495096
581	22	50134423	Hapmap57252-rs29018823	0.1034977
582	22	54250243	Hapmap41094-BTA-83358	0.1057006
583	22	54271229	ARS-BFGL-NGS-104424	0.1265988
584	22	54295406	ARS-BFGL-NGS-114255	0.1029561
585	22	54372266	ARS-BFGL-NGS-76826	0.1227542
586	22	54428720	ARS-BFGL-NGS-116073	0.1260568
587	22	54473931	ARS-BFGL-NGS-25024	0.1378796
588	22	54498985	ARS-BFGL-NGS-118899	0.1316562
589	22	54521697	ARS-BFGL-NGS-14331	0.1039582
590	23	13850106	ARS-BFGL-NGS-41214	0.1031059
591	23	13870700	ARS-BFGL-NGS-13554	0.1694231
592	23	14007398	ARS-BFGL-NGS-25089	0.2228361
593	23	14056765	ARS-BFGL-NGS-101393	0.1429902
594	23	14094944	ARS-BFGL-NGS-61941	0.1750782
595	23	14117386	ARS-BFGL-NGS-105087	0.1263643
596	23	14164329	ARS-BFGL-NGS-118763	0.1897243
597	23	14185501	ARS-BFGL-NGS-34854	0.2138806
598	23	14208471	ARS-BFGL-NGS-100006	0.1411246
599	23	14487014	ARS-BFGL-NGS-115470	0.1009143
600	23	31725204	UA-IFASA-6229	0.1461533
601	23	31914133	ARS-BFGL-BAC-5885	0.1230809
602	23	32057953	BTA-89233-no-rs	0.1379745
603	23	32103167	Hapmap58717-rs29026673	0.1310055
604	23	32348856	Hapmap29824-BTA-137304	0.1052270
605	23	32393438	BTA-56261-no-rs	0.1004241
606	24	10511916	Hapmap59628-rs29013651	0.1043119
607	24	10588074	ARS-BFGL-BAC-41582	0.1692753
608	24	10590106	BTB-01140405	0.1103188
609	24	10643239	BTB-01998747	0.1059278
610	24	10664383	ARS-BFGL-NGS-79951	0.1031230
611	24	10714691	UA-IFASA-7878	0.1349751
612	24	10777722	BTB-00881336	0.1106956
613	24	21783988	Hapmap38610-BTA-57584	0.1164771
614	24	21830890	ARS-BFGL-BAC-47350	0.1246663
615	24	28330492	ARS-BFGL-NGS-63856	0.1575590
616	24	28353262	BTB-01485885	0.3220210
617	24	28417928	ARS-BFGL-NGS-5141	0.5945940
618	24	28452709	BTA-97262-no-rs	0.4809272
619	24	28487771	ARS-BFGL-BAC-28665	0.6899732
620	24	28516684	Hapmap54981-rs29019846	0.7747524
621	24	28540641	BTB-01485274	0.8983644
622	24	28570245	Hapmap58887-rs29013502	0.7619213
623	24	28604672	BTB-01646599	0.5894579
624	24	28638911	ARS-BFGL-NGS-41140	0.4372706
625	24	28676366	ARS-BFGL-NGS-35716	0.2628811
626	24	28703207	BTB-00884791	0.1367035
627	24	28795202	BTB-00883964	0.1325472
628	24	50103163	ARS-BFGL-NGS-17892	0.1038007
629	24	50175324	ARS-BFGL-NGS-36484	0.1327060
630	24	50226252	ARS-BFGL-NGS-26178	0.1118154
631	24	58477861	ARS-BFGL-NGS-87044	0.1004297
632	24	58568769	ARS-BFGL-NGS-72174	0.1005084
633	24	58877604	ARS-BFGL-NGS-40380	0.1425136
634	24	58902927	ARS-BFGL-NGS-84525	0.1162363
635	24	59057936	ARS-BFGL-NGS-93817	0.1045038
636	24	60798822	ARS-BFGL-NGS-36906	0.1158569
637	24	60845513	ARS-BFGL-NGS-117690	0.1447707
638	24	60875114	ARS-BFGL-NGS-41126	0.1492293
639	24	60916827	ARS-BFGL-NGS-25933	0.1147830
640	24	61463290	Hapmap35425-SCAFFOLD305155_18811	0.1061165
641	25	5903547	Hapmap30152-BTC-053926	0.1405579
642	25	5926428	ARS-BFGL-NGS-26868	0.2050458
643	25	6004727	Hapmap51962-BTA-96856	0.1976910
644	25	6057840	Hapmap32191-BTC-019394	0.2420539
645	25	6080581	Hapmap31265-BTC-019439	0.2715531
646	25	6109723	Hapmap32438-BTC-019523	0.2017090
647	25	6130207	Hapmap24512-BTC-019555	0.1332877
648	25	6161712	Hapmap31908-BTC-019642	0.1197786
649	25	6193751	Hapmap30117-BTC-019731	0.1169914
650	25	688464	ARS-BFGL-NGS-108460	0.1508403
651	25	758528	ARS-BFGL-NGS-117233	0.2513333
652	25	814595	ARS-BFGL-NGS-62237	0.1867389
653	25	841307	ARS-BFGL-NGS-116934	0.1922123
654	25	925854	ARS-BFGL-NGS-115164	0.1735270
655	25	950550	ARS-BFGL-NGS-117981	0.1305691
656	25	986967	ARS-BFGL-NGS-4009	0.1112433
657	26	26675554	BTB-01856345	0.1126247
658	26	26716950	BTB-01731406	0.1803446
659	26	26805982	ARS-BFGL-NGS-10463	0.1468832
660	26	26835193	BTA-86472-no-rs	0.1422767
661	27	16546003	ARS-BFGL-NGS-97489	0.1281084
662	27	16628800	Hapmap4685-BTA-29671	0.1098274
663	27	16663290	BTB-00952622	0.1510777
664	27	16692516	ARS-BFGL-NGS-58358	0.1468467
665	27	16749902	ARS-BFGL-NGS-34807	0.1238276
666	27	16778234	ARS-BFGL-NGS-13660	0.1055917
667	27	16796562	UA-IFASA-3305	0.1176239
668	27	16818014	ARS-BFGL-NGS-38547	0.1158411
669	27	16946270	ARS-BFGL-NGS-45781	0.1033940
670	27	29096224	Hapmap44432-BTA-97711	0.1134651
671	27	29137254	ARS-BFGL-NGS-110668	0.1746578
672	27	29184261	BTB-01063707	0.2285980
673	27	29245816	Hapmap30049-BTA-160481	0.1872347
674	27	29300707	ARS-BFGL-NGS-107516	0.2000076
675	27	29327952	ARS-BFGL-NGS-58109	0.1936190
676	27	29356039	ARS-BFGL-NGS-5502	0.1790443
677	27	29388307	BTB-01063562	0.2038671
678	27	29427447	BTA-97696-no-rs	0.1708089
679	27	29450651	BTA-97695-no-rs	0.1139376
680	27	29476959	BTB-01063458	0.1223780
681	27	29960827	BTA-94629-no-rs	0.1111887
682	28	19756973	BTB-01016364	0.2043934
683	28	19811322	ARS-BFGL-NGS-45799	0.2368953
684	28	19890618	BTB-01681889	0.1636749
685	28	19935115	BTB-00980670	0.1467975
686	28	20968839	BTB-01129293	0.1021450
687	28	26144415	ARS-BFGL-NGS-4258	0.1036968
688	28	26311842	BTA-63833-no-rs	0.1209459
689	28	26344300	ARS-BFGL-NGS-102837	0.1309641
690	28	26372666	ARS-BFGL-NGS-36357	0.1178690
691	28	26414679	ARS-BFGL-NGS-119677	0.1280784
692	28	26472060	ARS-BFGL-NGS-116627	0.1148099
693	28	26509128	ARS-BFGL-NGS-20357	0.1071149
694	28	26598192	ARS-BFGL-NGS-4070	0.1539545
695	28	26627985	ARS-BFGL-NGS-107963	0.1150351
696	28	26661955	ARS-BFGL-NGS-1594	0.1050684
697	28	26683350	ARS-BFGL-NGS-80910	0.1202227
698	28	26705231	ARS-BFGL-NGS-30338	0.1171303
699	28	26728689	ARS-BFGL-NGS-116374	0.1116337
700	28	26752831	ARS-BFGL-NGS-44160	0.1153595
701	28	26770398	Hapmap47897-BTA-25453	0.1067212
702	28	32922685	Hapmap34673-BES10_Contig743_1320	0.1008108
703	28	3430196	Hapmap49856-BTA-108815	0.1025316
704	28	34836515	ARS-BFGL-NGS-103387	0.1396726
705	28	34851742	ARS-BFGL-NGS-65547	0.2196796
706	28	34875430	ARS-BFGL-NGS-15751	0.2534345
707	28	34912784	Hapmap47068-BTA-64117	0.2442533
708	28	34951758	ARS-BFGL-NGS-108719	0.2146477
709	28	34982582	ARS-BFGL-NGS-57340	0.2489700
710	28	35093950	BTB-00987935	0.2165726
711	28	35132199	ARS-USMARC-Parent-EF026086-rs29013660	0.3313140
712	28	35155098	UA-IFASA-5633	0.3037150
713	28	35188027	BTA-26221-no-rs	0.1835407
714	28	35212761	ARS-BFGL-NGS-36659	0.1099391
715	28	35466741	BTB-02015177	0.1249869
716	28	38623185	ARS-BFGL-NGS-110340	0.1346886
717	28	39341830	BTA-64258-no-rs	0.1630702
718	28	39384586	Hapmap41045-BTA-64263	0.1442320
719	28	39457322	BTA-64266-no-rs	0.1476647
720	28	39556851	ARS-BFGL-NGS-60152	0.1227265
721	28	39632576	BTB-00990573	0.1970464
722	28	39654559	BTB-01978062	0.1371528
723	28	39775835	ARS-BFGL-NGS-92658	0.1739983
724	28	39796396	ARS-BFGL-NGS-55590	0.1583936
725	29	46661667	ARS-BFGL-NGS-100175	0.1336997
726	29	46700354	ARS-BFGL-NGS-18176	0.1821689
727	29	46720868	ARS-BFGL-NGS-76567	0.1577159
728	29	46767514	Hapmap41327-BTA-66071	0.1337422
729	29	46799868	ARS-BFGL-NGS-61484	0.1060252
730	29	46823027	ARS-BFGL-NGS-101626	0.1164617
731	29	46844718	ARS-BFGL-NGS-14050	0.1281410
732	29	46871788	ARS-BFGL-NGS-107395	0.1072433
733	3	105566882	ARS-BFGL-NGS-111314	0.1417917
734	3	105587654	ARS-BFGL-NGS-30583	0.3004602
735	3	105633280	Hapmap22746-BTA-141712	0.1918849
736	3	105687436	BTB-00148881	0.1723418
737	3	105691182	BTB-00148908	0.1400318
738	3	105745609	INRA-111	0.1264987
739	3	105787741	BTB-01932810	0.1089950
740	3	105811284	ARS-BFGL-NGS-23119	0.1113716
741	3	110260420	BTB-00156546	0.1023462
742	3	110319557	ARS-BFGL-NGS-3489	0.1342032
743	3	110364783	BTB-00156662	0.1565742
744	3	110395482	BTB-00156691	0.1757510
745	3	110419349	BTB-00390861	0.1193027
746	3	110489512	BTB-00156807	0.1371208
747	3	110546593	BTA-01438-rs29012403	0.1154489
748	3	110571030	Hapmap58315-ss46527116	0.1170755
749	3	110631569	BTB-00156962	0.1139339
750	3	111526170	ARS-BFGL-NGS-74948	0.1193364
751	3	111551378	Hapmap30841-BTA-147966	0.1413166
752	3	111594054	BTA-69565-no-rs	0.2683995
753	3	111615734	BTA-69569-no-rs	0.1918510
754	3	111651143	Hapmap35643-SCAFFOLD318144_4314	0.3867037
755	3	111677167	BTB-01948148	0.7467347
756	3	111708236	Hapmap42062-BTA-109789	1.2415855
757	3	111730561	BTB-01641394	0.9480214
758	3	111751663	ARS-BFGL-NGS-37809	1.0607739
759	3	111772736	ARS-BFGL-NGS-25298	1.2101267
760	3	111806406	ARS-BFGL-NGS-44131	1.0252690
761	3	111833768	ARS-BFGL-NGS-6202	1.1671762
762	3	111933069	ARS-BFGL-NGS-85333	0.6077153
763	3	111965305	ARS-BFGL-NGS-97849	0.7547885
764	3	111991216	ARS-BFGL-NGS-71345	0.4965733
765	3	112064027	ARS-BFGL-NGS-110683	0.3321464
766	3	112192839	BTA-87451-no-rs	0.2441924
767	3	112221070	ARS-BFGL-NGS-18163	0.2095206
768	3	112257692	ARS-BFGL-NGS-115246	0.1130036
769	3	13457326	ARS-BFGL-NGS-3374	0.1417192
770	3	13623576	ARS-BFGL-NGS-106490	0.1089414
771	3	13646233	ARS-BFGL-NGS-108231	0.1087681
772	3	13692707	ARS-BFGL-NGS-119921	0.1247046
773	3	13768233	Hapmap53387-rs29010859	0.1744094
774	3	13927203	ARS-BFGL-NGS-1316	0.1854590
775	3	14015189	ARS-BFGL-NGS-6556	0.1526392
776	3	14037138	UA-IFASA-8925	0.1543209
777	3	14092136	Hapmap53344-ss46526153	0.1066961
778	3	22964033	Hapmap45949-BTA-66899	0.1053203
779	3	31771719	BTB-00125277	0.1012466
780	3	31834027	INRA-378	0.1338824
781	3	31874226	BTB-00125325	0.1198039
782	3	31896363	BTB-00125348	0.1165687
783	3	31919978	ARS-BFGL-NGS-111118	0.1360475
784	3	43199821	ARS-BFGL-NGS-102970	0.1005674
785	3	44209622	ARS-BFGL-NGS-33061	0.1159334
786	3	44270113	Hapmap32570-BTA-141315	0.1213717
787	3	44454674	INRA-624	0.1237071
788	3	44490132	ARS-BFGL-NGS-55002	0.1225157
789	3	44563542	INRA-615	0.1279236
790	3	61775858	Hapmap59991-rs29016955	0.1141671
791	3	77030221	BTB-01060056	0.1025089
792	3	89365301	BTB-00142159	0.1340868
793	3	89411264	ARS-BFGL-NGS-44533	0.1044862
794	3	89466489	BTB-00141705	0.1665189
795	3	89561376	BTB-01755444	0.1327578
796	3	89652649	Hapmap57748-ss46526142	0.1043548
797	3	89752439	Hapmap34238-BES1_Contig417_1330	0.1114883
798	3	96947843	ARS-BFGL-NGS-67035	0.1557861
799	3	96974610	BTB-00147847	0.1704952
800	3	96997280	ARS-BFGL-NGS-63629	0.1420519
801	3	97023337	BTB-00147905	0.1698968
802	3	97049079	BTA-26108-no-rs	0.1830603
803	3	97070925	BTB-01919893	0.2095165
804	3	97093984	Hapmap38905-BTA-68975	0.1928705
805	3	97141095	BTA-68980-no-rs	0.1400997
806	4	101775238	Hapmap51374-BTA-104594	0.1037509
807	4	102672717	BTA-85913-no-rs	0.1528300
808	4	102773397	BTA-72155-no-rs	0.1513353
809	4	102850747	BTA-72153-no-rs	0.1659194
810	4	102942082	Hapmap46021-BTA-72146	0.1754339
811	4	103014613	Hapmap60483-rs29010048	0.1778791
812	4	103046494	ARS-BFGL-NGS-109036	0.1573743
813	4	103081003	ARS-BFGL-NGS-32175	0.1194240
814	4	103142613	Hapmap51625-BTA-71992	0.1074542
815	4	106069205	ARS-BFGL-NGS-35219	0.1934954
816	4	106109340	Hapmap35759-SCAFFOLD161053_2988	0.1729248
817	4	106144190	Hapmap47387-BTA-72195	0.1533669
818	4	106191183	ARS-BFGL-NGS-4608	0.2170282
819	4	106217430	Hapmap51932-BTA-119886	0.2928994
820	4	106244569	ARS-BFGL-NGS-35727	0.2349577
821	4	106308774	Hapmap49723-BTA-72140	0.1849151
822	4	106435387	ARS-BFGL-NGS-100841	0.1562278
823	4	106536218	ARS-BFGL-NGS-28655	0.1234697
824	4	51114124	BTB-00184775	0.1606117
825	4	51149352	ARS-BFGL-NGS-117196	0.1553835
826	4	51179378	ARS-BFGL-NGS-113150	0.1091968
827	4	7192396	BTB-01511967	0.1497885
828	4	72119491	ARS-BFGL-NGS-42007	0.1122717
829	4	7225482	ARS-BFGL-NGS-6424	0.1546828
830	4	7295838	BTB-01580567	0.1703287
831	4	73095991	BTA-71353-no-rs	0.1089045
832	4	7372107	BTB-01580681	0.2131738
833	4	7402633	ARS-BFGL-NGS-57919	0.1844689
834	4	7427468	BTB-00685072	0.3386708
835	4	7455891	ARS-BFGL-NGS-71657	0.3278440
836	4	7510935	ARS-BFGL-NGS-106242	0.2017907
837	4	7564729	Hapmap29841-BTA-150778	0.1335090
838	4	7600537	ARS-BFGL-NGS-34749	0.1003906
839	5	48229383	ARS-BFGL-NGS-40753	0.1083676
840	5	52249518	Hapmap39861-BTA-73536	0.1196092
841	5	52357915	Hapmap43667-BTA-73533	0.1043698
842	5	52526291	ARS-BFGL-NGS-78314	0.1206889
843	5	53009069	ARS-BFGL-NGS-8741	0.1220420
844	5	53321576	BTA-99758-no-rs	0.1118884
845	5	53355038	ARS-BFGL-NGS-114150	0.1340222
846	5	53412863	Hapmap51300-BTA-73606	0.1084663
847	5	5493169	BTA-73200-no-rs	0.1155493
848	5	5516675	ARS-BFGL-NGS-3594	0.1099072
849	5	5566934	BTB-00215816	0.1871746
850	5	5593576	BTB-00215874	0.2145006
851	5	5621182	BTA-73127-no-rs	0.2243274
852	5	5660281	BTB-01200617	0.2257862
853	5	5684010	Hapmap40881-BTA-109195	0.2038843
854	5	5709307	ARS-BFGL-NGS-35982	0.1989244
855	5	5756462	ARS-BFGL-NGS-116916	0.2160151
856	5	5781953	BTB-01793309	0.1436056
857	5	5899712	BTB-01200493	0.1681461
858	5	6016429	BTB-01610675	0.3122917
859	5	6147730	UA-IFASA-4428	0.2459825
860	5	6234962	Hapmap27170-BTA-142780	0.2194508
861	5	6256982	BTA-73940-no-rs	0.2229995
862	5	6281168	Hapmap59616-ss46526976	0.3864947
863	5	6312610	BTA-73915-no-rs	0.5103844
864	5	6372558	ARS-BFGL-NGS-8671	0.2652141
865	5	6396764	ARS-BFGL-NGS-113945	0.2049050
866	5	6477926	Hapmap38248-BTA-73843	0.1570982
867	6	105650631	BTB-00280101	0.1070824
868	6	105694985	ARS-BFGL-NGS-80866	0.1557800
869	6	105733825	ARS-BFGL-NGS-112381	0.2480203
870	6	105837894	ARS-BFGL-NGS-34635	0.2939167
871	6	105860748	BTB-00280987	0.3147472
872	6	105886914	BTB-00280923	0.2237218
873	6	105973106	ARS-BFGL-NGS-117667	0.1631719
874	6	106019199	ARS-BFGL-NGS-69648	0.1470377
875	6	110389495	ARS-BFGL-NGS-103113	0.1151160
876	6	110442940	BTB-00282048	0.1186046
877	6	110482099	ARS-BFGL-NGS-34464	0.1186035
878	6	110542262	ARS-BFGL-NGS-54732	0.1144438
879	6	36441375	Hapmap26261-BTC-034133	0.1217517
880	6	36498019	ARS-BFGL-NGS-43280	0.1401323
881	6	36556442	BTA-22850-no-rs	0.1082570
882	6	40662051	Hapmap49744-BTA-75903	0.1011362
883	6	40696320	BTA-75902-no-rs	0.1051303
884	6	40780850	BTA-75891-no-rs	0.1015614
885	6	40808612	ARS-BFGL-NGS-90857	0.1481518
886	6	43494887	Hapmap24121-BTC-039009	0.1010038
887	6	43518582	Hapmap23242-BTC-039071	0.1338035
888	6	43544441	BTB-00254199	0.1465032
889	6	43579172	Hapmap31283-BTC-039096	0.1019958
890	6	43601154	Hapmap31284-BTC-039204	0.1712472
891	6	43657946	Hapmap47403-BTA-76048	0.1649421
892	6	43679917	Hapmap30420-BTC-039335	0.1664973
893	6	43721038	Hapmap32456-BTC-038385	0.1213865
894	6	43758146	Hapmap26267-BTC-039886	0.1582813
895	6	43805602	Hapmap32217-BTC-039812	0.1256920
896	6	43829217	Hapmap47098-BTA-76051	0.1311694
897	6	43886543	Hapmap31607-BTC-039637	0.1308108
898	6	43944737	ARS-BFGL-NGS-112073	0.1418697
899	6	43983354	ARS-BFGL-NGS-112781	0.1288952
900	6	45053450	Hapmap23226-BTA-159656	0.1035909
901	6	45112831	ARS-BFGL-NGS-21182	0.1046554
902	6	51093319	BTB-00257472	0.1037848
903	6	54231133	BTA-94705-no-rs	0.1015233
904	6	57842883	ARS-BFGL-NGS-100658	0.1086336
905	6	57929945	ARS-BFGL-NGS-31848	0.1425818
906	6	57984344	ARS-BFGL-NGS-106371	0.1783787
907	6	58038612	ARS-BFGL-NGS-25659	0.1745892
908	6	58077967	ARS-BFGL-NGS-34791	0.2052706
909	6	58148793	Hapmap26209-BTA-162290	0.2045594
910	6	58244833	BTA-97850-no-rs	0.2474062
911	6	58285461	Hapmap54653-rs29025767	0.1980535
912	6	58333974	BTA-97854-no-rs	0.1648925
913	6	58528228	Hapmap55170-rs29017690	0.1118230
914	6	61487026	BTB-01211574	0.1647696
915	6	61515144	ARS-BFGL-NGS-69759	0.1598941
916	6	61539588	BTB-01211501	0.1294167
917	6	61581893	BTB-01857634	0.1283944
918	6	61625983	ARS-BFGL-NGS-41007	0.1434674
919	6	61697427	Hapmap26181-BTA-156071	0.1285647
920	6	61752222	ARS-BFGL-NGS-103013	0.1193961
921	6	68896760	BTA-76705-no-rs	0.1103173
922	6	68932249	ARS-BFGL-NGS-92044	0.1666872
923	6	69030825	ARS-BFGL-NGS-47876	0.1320537
924	6	69054945	ARS-BFGL-NGS-40712	0.1097742
925	6	69278302	BTA-76627-no-rs	0.1008280
926	6	70387975	Hapmap32219-BTC-042322	0.1258112
927	6	70431058	Hapmap23983-BTC-070420	0.1183435
928	6	71703879	ARS-BFGL-NGS-106835	0.1882817
929	6	71795922	Hapmap51492-BTA-76637	0.2055839
930	6	71910070	BTB-00261083	0.2768335
931	6	71951614	ARS-BFGL-NGS-117025	0.2975765
932	6	71974040	Hapmap52482-ss46526125	0.2380617
933	6	72022899	Hapmap43042-BTA-76779	0.1927788
934	6	72092553	Hapmap24088-BTA-144179	0.2281654
935	6	72153601	BTB-00263396	0.2273603
936	6	72189556	BTB-00263355	0.1805114
937	6	72227266	ARS-BFGL-NGS-69314	0.1594467
938	6	72267108	Hapmap50775-BTA-76767	0.2035222
939	6	72349944	ARS-BFGL-NGS-55843	0.1029125
940	6	72385883	BTB-00263321	0.1734409
941	6	80246429	Hapmap24323-BTC-053517	0.1378393
942	6	80285424	Hapmap25412-BTC-031860	0.1167958
943	6	80405653	ARS-BFGL-NGS-114989	0.1172440
944	6	8553220	BTA-104962-no-rs	0.1316475
945	6	8630173	BTB-01068898	0.2313436
946	6	87090740	ARS-BFGL-NGS-17376	0.1239016
947	6	87266808	BTA-68275-no-rs	0.1021253
948	6	8727520	Hapmap33379-BTA-153882	0.1893140
949	6	87369626	BTA-22106-no-rs	0.1145328
950	6	87399244	Hapmap39786-BTA-86142	0.1079785
951	6	87437717	Hapmap51409-BTA-122717	0.1253506
952	6	87497222	ARS-BFGL-NGS-28041	0.1188147
953	6	8794456	Hapmap44182-BTA-104972	0.1886211
954	6	8881200	ARS-BFGL-NGS-54017	0.2055334
955	6	9049173	BTB-01645485	0.2081017
956	6	9142299	BTB-01069210	0.2346003
957	6	9166519	BTB-01069292	0.1925586
958	6	9189316	BTB-02016906	0.1212117
959	6	92805395	ARS-BFGL-NGS-32227	0.1054156
960	7	108633936	ARS-BFGL-NGS-16396	0.1536597
961	7	108657867	Hapmap53611-rs29011370	0.1006227
962	7	12380941	UA-IFASA-1667	0.1199814
963	7	19224812	ARS-BFGL-NGS-7483	0.1334098
964	7	19288283	ARS-BFGL-NGS-14531	0.1301182
965	7	19339413	ARS-BFGL-NGS-104684	0.1167100
966	7	19419542	BTB-00296762	0.1044469
967	7	19679690	ARS-BFGL-NGS-20402	0.1000973
968	7	30718129	BTB-00303128	0.1109728
969	7	30756401	BTA-78751-no-rs	0.1146857
970	7	30842216	BTB-00303295	0.1481546
971	7	30893983	ARS-BFGL-NGS-104708	0.2209091
972	7	31053463	BTB-00303899	0.1711271
973	7	31097653	Hapmap40159-BTA-104763	0.2077344
974	7	31159216	ARS-BFGL-NGS-77121	0.2198390
975	7	31184677	BTB-01571406	0.2276940
976	7	31220790	BTB-01518845	0.2285388
977	7	31271517	ARS-BFGL-NGS-64145	0.1682386
978	7	31322240	BTA-121460-no-rs	0.1225616
979	7	31343335	Hapmap57685-rs29016545	0.1170425
980	7	33854683	Hapmap44527-BTA-17793	0.1330843
981	7	33929533	UA-IFASA-4938	0.1227759
982	7	34096243	Hapmap42755-BTA-99683	0.1027943
983	7	34168848	ARS-BFGL-NGS-118984	0.1405064
984	7	34195094	ARS-BFGL-NGS-76864	0.1365810
985	7	34220737	Hapmap52098-rs29017346	0.1072547
986	7	51178102	Hapmap44410-BTA-78928	0.1162805
987	7	52464625	ARS-BFGL-NGS-15368	0.1284217
988	7	52731403	ARS-BFGL-NGS-11368	0.1460460
989	7	52773163	ARS-BFGL-NGS-3839	0.2226009
990	7	52843633	ARS-BFGL-NGS-114525	0.1205873
991	7	52886233	ARS-BFGL-NGS-105148	0.1449115
992	7	55135068	BTB-01175783	0.1146606
993	7	55186678	BTB-01175761	0.1803620
994	7	55234250	BTB-00311362	0.2442888
995	7	55393142	Hapmap54976-rs29019286	0.2443252
996	7	55416080	ARS-BFGL-NGS-22197	0.2226450
997	7	55541674	BTA-88472-no-rs	0.1533916
998	7	55567655	ARS-BFGL-NGS-65670	0.1557251
999	7	55613829	ARS-BFGL-NGS-107103	0.1697592
1000	7	55634474	BTB-01641665	0.1431795
1001	7	63935354	ARS-BFGL-NGS-6029	0.1536041
1002	7	64065612	BTA-28702-no-rs	0.1308118
1003	7	64160880	Hapmap35285-BES4_Contig405_551	0.2126799
1004	7	64216054	BTA-12698-rs29024650	0.1600284
1005	7	64258130	Hapmap41494-BTA-28685	0.2190532
1006	7	64285750	BTA-28679-no-rs	0.1992011
1007	7	64307708	BTB-01880776	0.1580737
1008	7	64332584	BTB-01880736	0.2196837
1009	7	64375368	BTA-28678-no-rs	0.1541118
1010	7	64398006	BTB-01105928	0.1080261
1011	7	72482132	BTB-01217667	0.1735299
1012	7	72518376	Hapmap50873-BTA-112610	0.2752413
1013	7	72563728	BTB-01217507	0.2337061
1014	7	72593664	BTB-01217472	0.2390748
1015	7	72621717	Hapmap48186-BTA-112617	0.2037375
1016	7	72657448	ARS-BFGL-NGS-37793	0.1674678
1017	7	72693116	ARS-BFGL-NGS-102365	0.1365016
1018	7	72828445	ARS-BFGL-NGS-107513	0.1127419
1019	7	72852260	Hapmap48995-BTA-103787	0.1183161
1020	7	73473535	Hapmap55033-rs29016821	0.1210329
1021	7	73507133	BTB-00319733	0.1520350
1022	7	73539906	BTB-00319743	0.1352916
1023	7	73574389	BTB-00319755	0.1163384
1024	7	73605674	BTB-00319838	0.1194297
1025	7	73607632	BTB-00319857	0.1257561
1026	7	73637074	Hapmap34667-BES3_Contig253_417	0.1501762
1027	7	90688919	ARS-BFGL-NGS-101886	0.1170778
1028	7	97603739	ARS-BFGL-NGS-42763	0.1486863
1029	7	97813968	BTB-00327062	0.1248129
1030	7	97980945	ARS-BFGL-NGS-96074	0.1293739
1031	8	25725277	BTB-01051883	0.1129557
1032	8	25753902	Hapmap58082-rs29010933	0.1830822
1033	8	25802651	BTB-01051794	0.2412612
1034	8	25830738	Hapmap49881-BTA-120681	0.1891495
1035	8	25858677	BTA-27527-no-rs	0.1284104
1036	8	36246265	ARS-BFGL-NGS-52657	0.1099826
1037	8	55004187	BTB-01820765	0.1480942
1038	8	55190206	Hapmap54208-rs29015846	0.1716331
1039	8	55218811	Hapmap54541-rs29014049	0.2549722
1040	8	55298755	BTA-107975-no-rs	0.1676213
1041	8	55323946	BTB-01616745	0.1450458
1042	8	55376512	ARS-BFGL-NGS-76732	0.1450588
1043	8	55433879	BTB-02017947	0.1047509
1044	8	65038546	ARS-BFGL-NGS-15846	0.1267777
1045	8	65108782	ARS-BFGL-NGS-111488	0.1127684
1046	8	65148173	BTB-00950285	0.1042636
1047	8	70893052	Hapmap39948-BTA-81787	0.1076592
1048	9	10112014	ARS-BFGL-NGS-57285	0.2085905
1049	9	10139748	ARS-BFGL-NGS-37889	0.1640947
1050	9	10221793	ARS-BFGL-NGS-93995	0.1460170
1051	9	32128416	BTB-00386288	0.1029364
1052	9	50084516	BTA-83709-no-rs	0.1075781
1053	9	50127680	UA-IFASA-4980	0.1619502
1054	9	50156167	BTB-00392462	0.1928335
1055	9	50193250	Hapmap49336-BTA-83713	0.1790577
1056	9	50223504	BTB-00392496	0.2264040
1057	9	50246135	Hapmap44146-BTA-83959	0.1558062
1058	9	50288536	BTB-00397090	0.2264369
1059	9	50316056	BTB-00397064	0.1677944
1060	9	50453292	ARS-BFGL-NGS-101365	0.1100639
1061	9	76708517	ARS-BFGL-NGS-116465	0.1330889
1062	9	76804356	ARS-BFGL-NGS-10386	0.1289856
1063	9	76843457	BTA-16148-no-rs	0.1179627
1064	9	87454872	BTB-02079832	0.1128078
1065	9	87701436	Hapmap58334-rs29012728	0.1102492
1066	9	87737922	ARS-BFGL-NGS-28183	0.1508794
1067	9	87761731	BTB-00865957	0.1274192
1068	9	87784500	BTA-56201-no-rs	0.1827472
1069	9	87813977	ARS-BFGL-NGS-6332	0.1002053
1070	9	87859682	Hapmap43079-BTA-84708	0.1233976
1071	9	88080504	Hapmap50128-BTA-84697	0.1210156
1072	9	88113326	ARS-BFGL-NGS-92851	0.1544752
1073	9	88160188	Hapmap58876-ss46526986	0.2696324
1074	9	88210465	BTB-00405541	0.2575876
1075	9	88254314	BTB-00405487	0.2377827
1076	9	88276948	ARS-BFGL-NGS-18187	0.2077461
1077	9	88414100	ARS-BFGL-NGS-97743	0.2119306
1078	9	88532524	ARS-BFGL-NGS-55625	0.1704414
1079	9	88561108	ARS-BFGL-NGS-66371	0.1986769
1080	9	88598336	BTB-00404975	0.1651226
1081	9	88636634	BTB-00404912	0.1047741
1082	9	9212931	ARS-BFGL-NGS-77146	0.1113366
1083	9	9302225	ARS-BFGL-NGS-100620	0.1757817
1084	9	9339314	Hapmap57776-rs29013550	0.1616223
1085	9	9595607	ARS-BFGL-NGS-85616	0.2346046
1086	9	96291120	ARS-BFGL-NGS-119644	0.1188891
1087	9	9643273	ARS-BFGL-NGS-5339	0.2329211
1088	9	9907258	Hapmap41153-BTA-110528	0.2362673
1089	9	9952764	ARS-BFGL-NGS-15799	0.2181269

9.4.1.1 Manhattan Plots for BLUPF90 Windows ssGWAS

# Set working directory
setwd("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10")
getwd()

# Read and process data
yyy1 = read.table("chrsnpvar")
yyy  = yyy1[order(yyy1$V4),]
zzz  = yyy[ which(yyy$V1==1 & yyy$V2==3), ]
n    = nrow(zzz)
y    = zzz[,4]
x    = zzz[,3]
chr1 = zzz[,5]
chr  = NULL
pos  = NULL
for (i in unique(yyy$V5)) {
  zz     = yyy[yyy$V5==i,]
  key    = zz$V4 
  medio  = round(nrow(zz)/2,0)
  z      = key[medio]
  pos    = c(pos,z)
}

# Assign colors for chromosomes
chrn       = unique(yyy$V5)
one        = which(chr1%%4==0) 
two        = which(chr1%%4==1) 
three      = which(chr1%%4==2) 
four       = which(chr1%%4==3) 
chr[one]   = "darkgoldenrod"
chr[two]   = "darkorchid"
chr[three] = "blue"
chr[four]  = "forestgreen"

# Generate Manhattan plot and save to PNG
png(file = "Vft1e3_manplot_with_thresholds.png", 
    width = 20000, height = 10000, res = 600) # Configure width, height, and resolution

# Set plot parameters
par(mfrow = c(1, 1), family = "sans", cex = 1.5, font = 2, mar = c(5, 5, 4, 2))

# Create Manhattan plot
plot(y, x, xaxt = "n", main = "Manhattan Plot SNP Variance explained by 10 adjacents SNP window", 
     xlab = "", ylab = "% variance expl", pch = 20, 
     xlim = c(1, n), ylim = c(0, max(x)), col = chr, cex.axis = 1.2)

# Add dashed lines for thresholds
abline(h = 0.1, col = "blue", lwd = 2, lty = 2) # Blue dashed line at 0.3
abline(h = 0.5, col = "red", lwd = 2, lty = 2)  # Red dashed line at 0.5

# Add legend for thresholds
legend("topright", legend = c("Threshold 0.1", "Threshold 0.5"), 
       col = c("blue", "red"), lwd = 2, lty = 2, cex = 1)

# Add chromosome labels on the X-axis
axis(1, at = pos, labels = chrn, las = 1, cex.axis = 0.8)

# Close the graphics device
dev.off()

My Image

9.4.2 0.5% Windows - BLUPF90+ GALLO

gwas <- read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w10_snp_rsid_snpvar_05.csv")
colnames(gwas)
colnames(gwas) <- c("X", "SNP", "rsID", "CHR", "BP", "Var")


# GALLO

#import a QTL annotation file
qtl_UCD1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Animal_QTLdb_release53_cattleARS_UCD1.gff.gz",file_type="gff")

#import a gene annotation file
gene_UDC1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Bos_taurus.ARS-UCD1.2.110.gtf.gz",file_type="gtf")

#FINDING GENES AND QTLs ARROUND THE MARKER

#FINDING GENES
out.genes <- find_genes_qtls_around_markers(db_file= gene_UDC1_2, 
                                            marker_file= gwas, 
                                            method = "gene",
                                            marker = "snp", 
                                            interval = 50000, 
                                            nThreads = NULL)

write.csv(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_genes_w05.csv")

#FINDING QTLs

out.qtl <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                          marker_file= gwas, 
                                          method = "qtl",
                                          marker = "snp", 
                                          interval = 50000, 
                                          nThreads = NULL)


write.table(out.qtl, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_qtl_w_05.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

library(tidyverse)
out.qtl.clean <- select(out.qtl, c("SNP", "rsID", "CHR", "QTL_type", "start_pos", "end_pos","QTL_ID"))
write.csv(out.qtl.clean, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_qtl_w_05_clean.csv")

The GALLO output are bellow:

For GENES

X.1	X	SNP	rsID	CHR	BP	Var	chr	start_pos	end_pos	width	strand	gene_id	gene_name	gene_biotype
1	1	ARS-BFGL-BAC-28665	rs111010562	24	28487771	0.6899732	24	28485983	28486143	161	+	ENSBTAG00000028575	U1	snRNA
2	42	Hapmap54981-rs29019846	rs29019846	24	28516684	0.7747524	24	28485983	28486143	161	+	ENSBTAG00000028575	U1	snRNA
3	4	ARS-BFGL-NGS-103753	rs110842922	2	115821065	1.1689686	2	115812583	115843935	31353	-	ENSBTAG00000021325	SLC19A3	protein_coding
4	41	Hapmap54770-rs29009608	rs29009608	2	115875702	0.7398417	2	115812583	115843935	31353	-	ENSBTAG00000021325	SLC19A3	protein_coding
5	4	ARS-BFGL-NGS-103753	rs110842922	2	115821065	1.1689686	2	115838391	115840022	1632	+	ENSBTAG00000007127	NA	protein_coding
6	41	Hapmap54770-rs29009608	rs29009608	2	115875702	0.7398417	2	115838391	115840022	1632	+	ENSBTAG00000007127	NA	protein_coding
7	11	ARS-BFGL-NGS-43721	rs108974471	2	115986085	0.8178265	2	115948258	115951955	3698	+	ENSBTAG00000021326	CCL20	protein_coding
8	11	ARS-BFGL-NGS-43721	rs108974471	2	115986085	0.8178265	2	115992779	116028253	35475	+	ENSBTAG00000021327	DAW1	protein_coding
9	5	ARS-BFGL-NGS-107330	rs109766798	2	116018639	0.5958551	2	115992779	116028253	35475	+	ENSBTAG00000021327	DAW1	protein_coding
10	37	Hapmap41888-BTA-49091	rs41645223	2	115622067	0.8268392	2	115629949	115704725	74777	+	ENSBTAG00000021323	AGFG1	protein_coding
11	2	ARS-BFGL-BAC-35548	rs110100182	2	115665427	1.2851806	2	115629949	115704725	74777	+	ENSBTAG00000021323	AGFG1	protein_coding
12	21	BTA-49096-no-rs	rs41578131	2	115695003	1.6071883	2	115629949	115704725	74777	+	ENSBTAG00000021323	AGFG1	protein_coding
13	8	ARS-BFGL-NGS-30337	rs110485060	2	115730530	1.3488469	2	115629949	115704725	74777	+	ENSBTAG00000021323	AGFG1	protein_coding
14	8	ARS-BFGL-NGS-30337	rs110485060	2	115730530	1.3488469	2	115758046	115758105	60	+	ENSBTAG00000054571	bta-mir-2285ao-1	miRNA
15	4	ARS-BFGL-NGS-103753	rs110842922	2	115821065	1.1689686	2	115791504	115808606	17103	+	ENSBTAG00000050771	NA	lncRNA
16	40	Hapmap50266-BTA-13664	rs29018622	13	18266073	0.6006650	13	18226732	18280982	54251	-	ENSBTAG00000016060	CREM	protein_coding
17	39	Hapmap49833-BTA-103929	rs41603335	13	18386942	0.5282867	13	18321888	18393476	71589	+	ENSBTAG00000007133	CUL2	protein_coding
18	20	BTA-25900-no-rs	rs41575397	13	18509812	0.6364128	13	18449975	18466359	16385	+	ENSBTAG00000052242	NA	lncRNA
19	20	BTA-25900-no-rs	rs41575397	13	18509812	0.6364128	13	18484975	19062784	577810	+	ENSBTAG00000014991	PARD3	protein_coding
20	3	ARS-BFGL-BAC-7444	rs110491621	13	18558540	0.6630789	13	18484975	19062784	577810	+	ENSBTAG00000014991	PARD3	protein_coding
21	35	BTB-01948148	rs43056622	3	111677167	0.7467347	3	111603940	112289188	685249	+	ENSBTAG00000005784	CSMD2	protein_coding
22	38	Hapmap42062-BTA-109789	rs41621207	3	111708236	1.2415855	3	111603940	112289188	685249	+	ENSBTAG00000005784	CSMD2	protein_coding
23	31	BTB-01641394	rs42752353	3	111730561	0.9480214	3	111603940	112289188	685249	+	ENSBTAG00000005784	CSMD2	protein_coding
24	10	ARS-BFGL-NGS-37809	rs42751504	3	111751663	1.0607739	3	111603940	112289188	685249	+	ENSBTAG00000005784	CSMD2	protein_coding
25	6	ARS-BFGL-NGS-25298	rs109868537	3	111772736	1.2101267	3	111603940	112289188	685249	+	ENSBTAG00000005784	CSMD2	protein_coding
26	12	ARS-BFGL-NGS-44131	rs110100483	3	111806406	1.0252690	3	111603940	112289188	685249	+	ENSBTAG00000005784	CSMD2	protein_coding
27	15	ARS-BFGL-NGS-6202	rs110385521	3	111833768	1.1671762	3	111603940	112289188	685249	+	ENSBTAG00000005784	CSMD2	protein_coding
28	17	ARS-BFGL-NGS-85333	rs110742206	3	111933069	0.6077153	3	111603940	112289188	685249	+	ENSBTAG00000005784	CSMD2	protein_coding
29	18	ARS-BFGL-NGS-97849	rs110553601	3	111965305	0.7547885	3	111603940	112289188	685249	+	ENSBTAG00000005784	CSMD2	protein_coding
30	17	ARS-BFGL-NGS-85333	rs110742206	3	111933069	0.6077153	3	111927003	111927727	725	-	ENSBTAG00000000335	HMGB4	protein_coding
31	18	ARS-BFGL-NGS-97849	rs110553601	3	111965305	0.7547885	3	111927003	111927727	725	-	ENSBTAG00000000335	HMGB4	protein_coding
32	7	ARS-BFGL-NGS-2713	rs41761360	15	34054485	0.5549892	15	34027052	34225316	198265	+	ENSBTAG00000001410	GRAMD1B	protein_coding
33	19	ARS-BFGL-NGS-98724	rs109709275	15	34109962	0.6252263	15	34027052	34225316	198265	+	ENSBTAG00000001410	GRAMD1B	protein_coding
34	14	ARS-BFGL-NGS-5976	rs41763278	15	34144843	0.5490537	15	34027052	34225316	198265	+	ENSBTAG00000001410	GRAMD1B	protein_coding
35	22	BTA-73915-no-rs	rs41648979	5	6312610	0.5103844	5	6174722	6273171	98450	+	ENSBTAG00000021756	ZDHHC17	protein_coding
36	22	BTA-73915-no-rs	rs41648979	5	6312610	0.5103844	5	6281008	6300975	19968	-	ENSBTAG00000013406	CSRP2	protein_coding
37	28	BTB-01434227	rs42557533	10	50554831	0.6660810	10	50557150	50607672	50523	+	ENSBTAG00000013493	BNIP2	protein_coding
38	28	BTB-01434227	rs42557533	10	50554831	0.6660810	10	50582585	50614948	32364	+	ENSBTAG00000010298	GTF2A2	protein_coding
39	28	BTB-01434227	rs42557533	10	50554831	0.6660810	10	50584841	50584967	127	-	ENSBTAG00000043816	NA	snoRNA

FOR QTLs

X	SNP	rsID	CHR	QTL_type	start_pos	end_pos	QTL_ID
1	ARS-BFGL-BAC-28665	rs111010562	24	Meat_and_Carcass	28473214	28473218	232857
2	Hapmap54981-rs29019846	rs29019846	24	Meat_and_Carcass	28473214	28473218	232857
3	BTB-01485274	rs42609685	24	Health	28570243	28570247	57038
4	Hapmap58887-rs29013502	rs29013502	24	Health	28570243	28570247	57038
5	BTB-01646599	rs42761380	24	Health	28570243	28570247	57038
6	BTB-01485274	rs42609685	24	Health	28570243	28570247	57040
7	Hapmap58887-rs29013502	rs29013502	24	Health	28570243	28570247	57040
8	BTB-01646599	rs42761380	24	Health	28570243	28570247	57040
9	BTB-01485274	rs42609685	24	Production	28570243	28570247	69281
10	Hapmap58887-rs29013502	rs29013502	24	Production	28570243	28570247	69281
11	BTB-01646599	rs42761380	24	Production	28570243	28570247	69281
12	Hapmap58887-rs29013502	rs29013502	24	Reproduction	28604670	28604674	138598
13	BTB-01646599	rs42761380	24	Reproduction	28604670	28604674	138598
14	ARS-BFGL-BAC-35548	rs110100182	2	Production	115695001	115695005	283322
15	BTA-49096-no-rs	rs41578131	2	Production	115695001	115695005	283322
16	ARS-BFGL-NGS-30337	rs110485060	2	Production	115695001	115695005	283322
17	ARS-BFGL-NGS-103753	rs110842922	2	Milk	115820324	115820328	215425
18	ARS-BFGL-NGS-103753	rs110842922	2	Exterior	115839213	115839217	125900
19	Hapmap54770-rs29009608	rs29009608	2	Exterior	115839213	115839217	125900
20	Hapmap54770-rs29009608	rs29009608	2	Milk	115909335	115909339	155904
21	Hapmap54770-rs29009608	rs29009608	2	Milk	115909359	115909363	155914
22	ARS-BFGL-NGS-43721	rs108974471	2	Production	115986083	115986087	39013
23	ARS-BFGL-NGS-107330	rs109766798	2	Production	115986083	115986087	39013
24	ARS-BFGL-NGS-43721	rs108974471	2	Reproduction	115986083	115986087	39014
25	ARS-BFGL-NGS-107330	rs109766798	2	Reproduction	115986083	115986087	39014
26	ARS-BFGL-NGS-43721	rs108974471	2	Exterior	115986083	115986087	39015
27	ARS-BFGL-NGS-107330	rs109766798	2	Exterior	115986083	115986087	39015
28	ARS-BFGL-NGS-43721	rs108974471	2	Exterior	115986083	115986087	39016
29	ARS-BFGL-NGS-107330	rs109766798	2	Exterior	115986083	115986087	39016
30	ARS-BFGL-NGS-43721	rs108974471	2	Milk	115986083	115986087	39017
31	ARS-BFGL-NGS-107330	rs109766798	2	Milk	115986083	115986087	39017
32	ARS-BFGL-NGS-43721	rs108974471	2	Production	115986083	115986087	39018
33	ARS-BFGL-NGS-107330	rs109766798	2	Production	115986083	115986087	39018
34	ARS-BFGL-NGS-43721	rs108974471	2	Exterior	115986083	115986087	39019
35	ARS-BFGL-NGS-107330	rs109766798	2	Exterior	115986083	115986087	39019
36	ARS-BFGL-NGS-43721	rs108974471	2	Milk	115986083	115986087	39020
37	ARS-BFGL-NGS-107330	rs109766798	2	Milk	115986083	115986087	39020
38	ARS-BFGL-NGS-43721	rs108974471	2	Production	115986083	115986087	39021
39	ARS-BFGL-NGS-107330	rs109766798	2	Production	115986083	115986087	39021
40	ARS-BFGL-NGS-43721	rs108974471	2	Production	115986083	115986087	39022
41	ARS-BFGL-NGS-107330	rs109766798	2	Production	115986083	115986087	39022
42	ARS-BFGL-NGS-43721	rs108974471	2	Milk	115986083	115986087	39023
43	ARS-BFGL-NGS-107330	rs109766798	2	Milk	115986083	115986087	39023
44	ARS-BFGL-NGS-43721	rs108974471	2	Milk	115986083	115986087	39024
45	ARS-BFGL-NGS-107330	rs109766798	2	Milk	115986083	115986087	39024
46	ARS-BFGL-NGS-43721	rs108974471	2	Exterior	115986083	115986087	39025
47	ARS-BFGL-NGS-107330	rs109766798	2	Exterior	115986083	115986087	39025
48	ARS-BFGL-NGS-43721	rs108974471	2	Exterior	115986083	115986087	39026
49	ARS-BFGL-NGS-107330	rs109766798	2	Exterior	115986083	115986087	39026
50	ARS-BFGL-NGS-43721	rs108974471	2	Reproduction	115986083	115986087	39027
51	ARS-BFGL-NGS-107330	rs109766798	2	Reproduction	115986083	115986087	39027
52	ARS-BFGL-NGS-43721	rs108974471	2	Exterior	115986083	115986087	39028
53	ARS-BFGL-NGS-107330	rs109766798	2	Exterior	115986083	115986087	39028
54	Hapmap49833-BTA-103929	rs41603335	13	Milk	18340131	18340135	116582
55	Hapmap49833-BTA-103929	rs41603335	13	Milk	18349683	18349687	116735
56	Hapmap49833-BTA-103929	rs41603335	13	Milk	18370805	18370809	249726
57	Hapmap49833-BTA-103929	rs41603335	13	Milk	18381731	18381735	116525
58	Hapmap49833-BTA-103929	rs41603335	13	Reproduction	18386940	18386944	46808
59	Hapmap49833-BTA-103929	rs41603335	13	Reproduction	18386940	18386944	46809
60	Hapmap49833-BTA-103929	rs41603335	13	Exterior	18386940	18386944	46810
61	Hapmap49833-BTA-103929	rs41603335	13	Exterior	18386940	18386944	46811
62	Hapmap49833-BTA-103929	rs41603335	13	Production	18386940	18386944	46812
63	Hapmap49833-BTA-103929	rs41603335	13	Exterior	18386940	18386944	46813
64	Hapmap49833-BTA-103929	rs41603335	13	Milk	18386940	18386944	46814
65	Hapmap49833-BTA-103929	rs41603335	13	Production	18386940	18386944	46815
66	Hapmap49833-BTA-103929	rs41603335	13	Production	18386940	18386944	46816
67	Hapmap49833-BTA-103929	rs41603335	13	Exterior	18386940	18386944	46817
68	Hapmap49833-BTA-103929	rs41603335	13	Exterior	18386940	18386944	46818
69	Hapmap49833-BTA-103929	rs41603335	13	Exterior	18386940	18386944	46819
70	Hapmap49833-BTA-103929	rs41603335	13	Reproduction	18386940	18386944	46820
71	Hapmap49833-BTA-103929	rs41603335	13	Reproduction	18386940	18386944	46821
72	Hapmap49833-BTA-103929	rs41603335	13	Exterior	18386940	18386944	46822
73	Hapmap49833-BTA-103929	rs41603335	13	Meat_and_Carcass	18386940	18386944	151596
74	Hapmap49833-BTA-103929	rs41603335	13	Meat_and_Carcass	18392222	18392226	226401
75	Hapmap49833-BTA-103929	rs41603335	13	Meat_and_Carcass	18392222	18392226	228662
76	Hapmap49833-BTA-103929	rs41603335	13	Meat_and_Carcass	18392222	18392226	234093
77	Hapmap49833-BTA-103929	rs41603335	13	Milk	18406821	18406825	116526
78	BTA-25900-no-rs	rs41575397	13	Reproduction	18489668	18489672	46823
79	BTA-25900-no-rs	rs41575397	13	Exterior	18489668	18489672	46824
80	BTA-25900-no-rs	rs41575397	13	Exterior	18489668	18489672	46825
81	BTA-25900-no-rs	rs41575397	13	Milk	18489668	18489672	46826
82	BTA-25900-no-rs	rs41575397	13	Production	18489668	18489672	46827
83	BTA-25900-no-rs	rs41575397	13	Exterior	18489668	18489672	46828
84	BTA-25900-no-rs	rs41575397	13	Exterior	18489668	18489672	46829
85	BTA-25900-no-rs	rs41575397	13	Production	18489668	18489672	46830
86	BTA-25900-no-rs	rs41575397	13	Production	18489668	18489672	46831
87	BTA-25900-no-rs	rs41575397	13	Exterior	18489668	18489672	46832
88	BTA-25900-no-rs	rs41575397	13	Exterior	18489668	18489672	46833
89	BTA-25900-no-rs	rs41575397	13	Reproduction	18489668	18489672	46834
90	BTA-25900-no-rs	rs41575397	13	Reproduction	18489668	18489672	46835
91	BTA-25900-no-rs	rs41575397	13	Exterior	18489668	18489672	46836
92	BTA-25900-no-rs	rs41575397	13	Reproduction	18509810	18509814	46837
93	ARS-BFGL-BAC-7444	rs110491621	13	Reproduction	18509810	18509814	46837
94	BTA-25900-no-rs	rs41575397	13	Reproduction	18509810	18509814	46838
95	ARS-BFGL-BAC-7444	rs110491621	13	Reproduction	18509810	18509814	46838
96	BTA-25900-no-rs	rs41575397	13	Exterior	18509810	18509814	46839
97	ARS-BFGL-BAC-7444	rs110491621	13	Exterior	18509810	18509814	46839
98	BTA-25900-no-rs	rs41575397	13	Exterior	18509810	18509814	46840
99	ARS-BFGL-BAC-7444	rs110491621	13	Exterior	18509810	18509814	46840
100	BTA-25900-no-rs	rs41575397	13	Production	18509810	18509814	46841
101	ARS-BFGL-BAC-7444	rs110491621	13	Production	18509810	18509814	46841
102	BTA-25900-no-rs	rs41575397	13	Exterior	18509810	18509814	46842
103	ARS-BFGL-BAC-7444	rs110491621	13	Exterior	18509810	18509814	46842
104	BTA-25900-no-rs	rs41575397	13	Exterior	18509810	18509814	46843
105	ARS-BFGL-BAC-7444	rs110491621	13	Exterior	18509810	18509814	46843
106	BTA-25900-no-rs	rs41575397	13	Production	18509810	18509814	46844
107	ARS-BFGL-BAC-7444	rs110491621	13	Production	18509810	18509814	46844
108	BTA-25900-no-rs	rs41575397	13	Production	18509810	18509814	46845
109	ARS-BFGL-BAC-7444	rs110491621	13	Production	18509810	18509814	46845
110	BTA-25900-no-rs	rs41575397	13	Exterior	18509810	18509814	46846
111	ARS-BFGL-BAC-7444	rs110491621	13	Exterior	18509810	18509814	46846
112	BTA-25900-no-rs	rs41575397	13	Exterior	18509810	18509814	46847
113	ARS-BFGL-BAC-7444	rs110491621	13	Exterior	18509810	18509814	46847
114	BTA-25900-no-rs	rs41575397	13	Exterior	18509810	18509814	46848
115	ARS-BFGL-BAC-7444	rs110491621	13	Exterior	18509810	18509814	46848
116	BTA-25900-no-rs	rs41575397	13	Exterior	18509810	18509814	46849
117	ARS-BFGL-BAC-7444	rs110491621	13	Exterior	18509810	18509814	46849
118	BTA-25900-no-rs	rs41575397	13	Reproduction	18509810	18509814	46850
119	ARS-BFGL-BAC-7444	rs110491621	13	Reproduction	18509810	18509814	46850
120	BTA-25900-no-rs	rs41575397	13	Health	18509810	18509814	46851
121	ARS-BFGL-BAC-7444	rs110491621	13	Health	18509810	18509814	46851
122	BTA-25900-no-rs	rs41575397	13	Reproduction	18509810	18509814	46852
123	ARS-BFGL-BAC-7444	rs110491621	13	Reproduction	18509810	18509814	46852
124	BTA-25900-no-rs	rs41575397	13	Exterior	18509810	18509814	46853
125	ARS-BFGL-BAC-7444	rs110491621	13	Exterior	18509810	18509814	46853
126	BTA-25900-no-rs	rs41575397	13	Reproduction	18558538	18558542	46854
127	ARS-BFGL-BAC-7444	rs110491621	13	Reproduction	18558538	18558542	46854
128	BTA-25900-no-rs	rs41575397	13	Exterior	18558538	18558542	46855
129	ARS-BFGL-BAC-7444	rs110491621	13	Exterior	18558538	18558542	46855
130	BTA-25900-no-rs	rs41575397	13	Exterior	18558538	18558542	46856
131	ARS-BFGL-BAC-7444	rs110491621	13	Exterior	18558538	18558542	46856
132	BTA-25900-no-rs	rs41575397	13	Milk	18558538	18558542	46857
133	ARS-BFGL-BAC-7444	rs110491621	13	Milk	18558538	18558542	46857
134	BTA-25900-no-rs	rs41575397	13	Exterior	18558538	18558542	46858
135	ARS-BFGL-BAC-7444	rs110491621	13	Exterior	18558538	18558542	46858
136	BTA-25900-no-rs	rs41575397	13	Milk	18558538	18558542	46859
137	ARS-BFGL-BAC-7444	rs110491621	13	Milk	18558538	18558542	46859
138	BTA-25900-no-rs	rs41575397	13	Production	18558538	18558542	46860
139	ARS-BFGL-BAC-7444	rs110491621	13	Production	18558538	18558542	46860
140	BTA-25900-no-rs	rs41575397	13	Production	18558538	18558542	46861
141	ARS-BFGL-BAC-7444	rs110491621	13	Production	18558538	18558542	46861
142	BTA-25900-no-rs	rs41575397	13	Milk	18558538	18558542	46862
143	ARS-BFGL-BAC-7444	rs110491621	13	Milk	18558538	18558542	46862
144	BTA-25900-no-rs	rs41575397	13	Exterior	18558538	18558542	46863
145	ARS-BFGL-BAC-7444	rs110491621	13	Exterior	18558538	18558542	46863
146	BTA-25900-no-rs	rs41575397	13	Exterior	18558538	18558542	46864
147	ARS-BFGL-BAC-7444	rs110491621	13	Exterior	18558538	18558542	46864
148	BTA-25900-no-rs	rs41575397	13	Reproduction	18558538	18558542	46865
149	ARS-BFGL-BAC-7444	rs110491621	13	Reproduction	18558538	18558542	46865
150	BTA-25900-no-rs	rs41575397	13	Reproduction	18558538	18558542	46866
151	ARS-BFGL-BAC-7444	rs110491621	13	Reproduction	18558538	18558542	46866
152	BTA-25900-no-rs	rs41575397	13	Exterior	18558538	18558542	46867
153	ARS-BFGL-BAC-7444	rs110491621	13	Exterior	18558538	18558542	46867
154	BTA-25900-no-rs	rs41575397	13	Exterior	18558538	18558542	46868
155	ARS-BFGL-BAC-7444	rs110491621	13	Exterior	18558538	18558542	46868
156	BTB-01948148	rs43056622	3	Reproduction	111708234	111708238	30008
157	Hapmap42062-BTA-109789	rs41621207	3	Reproduction	111708234	111708238	30008
158	BTB-01641394	rs42752353	3	Reproduction	111708234	111708238	30008
159	ARS-BFGL-NGS-37809	rs42751504	3	Reproduction	111708234	111708238	30008
160	BTB-01948148	rs43056622	3	Reproduction	111708234	111708238	30244
161	Hapmap42062-BTA-109789	rs41621207	3	Reproduction	111708234	111708238	30244
162	BTB-01641394	rs42752353	3	Reproduction	111708234	111708238	30244
163	ARS-BFGL-NGS-37809	rs42751504	3	Reproduction	111708234	111708238	30244
164	BTB-01948148	rs43056622	3	Meat_and_Carcass	111708234	111708238	152258
165	Hapmap42062-BTA-109789	rs41621207	3	Meat_and_Carcass	111708234	111708238	152258
166	BTB-01641394	rs42752353	3	Meat_and_Carcass	111708234	111708238	152258
167	ARS-BFGL-NGS-37809	rs42751504	3	Meat_and_Carcass	111708234	111708238	152258
168	BTB-01948148	rs43056622	3	Meat_and_Carcass	111717521	111717525	225527
169	Hapmap42062-BTA-109789	rs41621207	3	Meat_and_Carcass	111717521	111717525	225527
170	BTB-01641394	rs42752353	3	Meat_and_Carcass	111717521	111717525	225527
171	ARS-BFGL-NGS-37809	rs42751504	3	Meat_and_Carcass	111717521	111717525	225527
172	ARS-BFGL-NGS-97849	rs110553601	3	Meat_and_Carcass	111991214	111991218	151880
173	ARS-BFGL-NGS-97849	rs110553601	3	Meat_and_Carcass	111991214	111991218	152096
174	BTB-02063964	rs43172105	15	Reproduction	10936000	10936004	47797
175	BTB-01830390	rs42938737	15	Reproduction	10936000	10936004	47797
176	BTB-01608944	rs42723390	15	Reproduction	10936000	10936004	47797
177	BTB-02063964	rs43172105	15	Reproduction	10936000	10936004	47798
178	BTB-01830390	rs42938737	15	Reproduction	10936000	10936004	47798
179	BTB-01608944	rs42723390	15	Reproduction	10936000	10936004	47798
180	BTB-02063964	rs43172105	15	Exterior	10936000	10936004	47799
181	BTB-01830390	rs42938737	15	Exterior	10936000	10936004	47799
182	BTB-01608944	rs42723390	15	Exterior	10936000	10936004	47799
183	BTB-02063964	rs43172105	15	Milk	10936000	10936004	47800
184	BTB-01830390	rs42938737	15	Milk	10936000	10936004	47800
185	BTB-01608944	rs42723390	15	Milk	10936000	10936004	47800
186	BTB-02063964	rs43172105	15	Milk	10936000	10936004	47801
187	BTB-01830390	rs42938737	15	Milk	10936000	10936004	47801
188	BTB-01608944	rs42723390	15	Milk	10936000	10936004	47801
189	BTB-02063964	rs43172105	15	Production	10936000	10936004	47802
190	BTB-01830390	rs42938737	15	Production	10936000	10936004	47802
191	BTB-01608944	rs42723390	15	Production	10936000	10936004	47802
192	BTB-02063964	rs43172105	15	Production	10936000	10936004	47803
193	BTB-01830390	rs42938737	15	Production	10936000	10936004	47803
194	BTB-01608944	rs42723390	15	Production	10936000	10936004	47803
195	BTB-02063964	rs43172105	15	Milk	10936000	10936004	47804
196	BTB-01830390	rs42938737	15	Milk	10936000	10936004	47804
197	BTB-01608944	rs42723390	15	Milk	10936000	10936004	47804
198	BTB-02063964	rs43172105	15	Milk	10936000	10936004	47805
199	BTB-01830390	rs42938737	15	Milk	10936000	10936004	47805
200	BTB-01608944	rs42723390	15	Milk	10936000	10936004	47805
201	BTB-02063964	rs43172105	15	Reproduction	10936000	10936004	47806
202	BTB-01830390	rs42938737	15	Reproduction	10936000	10936004	47806
203	BTB-01608944	rs42723390	15	Reproduction	10936000	10936004	47806
204	BTB-02063964	rs43172105	15	Health	10936000	10936004	47807
205	BTB-01830390	rs42938737	15	Health	10936000	10936004	47807
206	BTB-01608944	rs42723390	15	Health	10936000	10936004	47807
207	BTB-02063964	rs43172105	15	Reproduction	10936000	10936004	47808
208	BTB-01830390	rs42938737	15	Reproduction	10936000	10936004	47808
209	BTB-01608944	rs42723390	15	Reproduction	10936000	10936004	47808
210	BTB-02063964	rs43172105	15	Exterior	10936000	10936004	47809
211	BTB-01830390	rs42938737	15	Exterior	10936000	10936004	47809
212	BTB-01608944	rs42723390	15	Exterior	10936000	10936004	47809
213	BTB-02063964	rs43172105	15	Exterior	10936000	10936004	47810
214	BTB-01830390	rs42938737	15	Exterior	10936000	10936004	47810
215	BTB-01608944	rs42723390	15	Exterior	10936000	10936004	47810
216	BTB-02063964	rs43172105	15	Exterior	10936000	10936004	47811
217	BTB-01830390	rs42938737	15	Exterior	10936000	10936004	47811
218	BTB-01608944	rs42723390	15	Exterior	10936000	10936004	47811
219	BTB-02063964	rs43172105	15	Reproduction	10936000	10936004	281490
220	BTB-01830390	rs42938737	15	Reproduction	10936000	10936004	281490
221	BTB-01608944	rs42723390	15	Reproduction	10936000	10936004	281490
222	BTB-01421892	rs42544714	15	Meat_and_Carcass	11198692	11198696	226564
223	BTB-01421934	rs42545356	15	Meat_and_Carcass	11198692	11198696	226564
224	BTB-01422008	rs42545430	15	Meat_and_Carcass	11198692	11198696	226564
225	ARS-BFGL-NGS-2713	rs41761360	15	Meat_and_Carcass	34025602	34025606	152600
226	ARS-BFGL-NGS-98724	rs109709275	15	Reproduction	34139603	34139607	62361
227	ARS-BFGL-NGS-5976	rs41763278	15	Reproduction	34139603	34139607	62361
228	ARS-BFGL-NGS-98724	rs109709275	15	Reproduction	34139603	34139607	62407
229	ARS-BFGL-NGS-5976	rs41763278	15	Reproduction	34139603	34139607	62407
230	ARS-BFGL-NGS-98724	rs109709275	15	Reproduction	34139603	34139607	62411
231	ARS-BFGL-NGS-5976	rs41763278	15	Reproduction	34139603	34139607	62411
232	ARS-BFGL-NGS-5976	rs41763278	15	Milk	34161889	34161893	155975
233	ARS-BFGL-NGS-5976	rs41763278	15	Reproduction	34164294	34164298	62410
234	ARS-BFGL-NGS-3276	rs110634531	20	Health	12088254	12088258	179019
235	ARS-BFGL-NGS-78615	rs110959523	20	Health	12088254	12088258	179019
236	BTA-73915-no-rs	rs41648979	5	Reproduction	6318394	6318398	212544
237	BTB-01434227	rs42557533	10	Health	50597586	50597590	156580

QTL Plots

# Set working directory
setwd("/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10")

#QTL type plot
oldpar <- par(mar=c(1,15,0.5,1))
plot_qtl_info(out.qtl, qtl_plot = "qtl_type", cex=1.5)

#QTL names plot (by type)

# Set the width and height in pixels for 600 DPI
png("qtl_names_w05.png", width=5100, height=6600, res=600)  # Width and height in pixels
# Set layout for multiple plots
par(mfrow=c(6, 1), mar=c(2, 20, 1, 1))
# List of QTL classes for titles
qtl_classes <- c("Production", "Reproduction", "Milk", "Meat_and_Carcass", "Health", "Exterior")
# Loop through each QTL class and plot
for (qtl_class in qtl_classes) {
  plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class=qtl_class)
  title(main=qtl_class)  # Add the QTL class as the main title for each plot
}
# Close the device
dev.off()


#QTL enrichment analysis 

out.enrich_qtl_name <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "Name",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)


# Sorting the dataframe in ascending order of adj.pval
sorted_df <- out.enrich_qtl_name[order(out.enrich_qtl_name$adj.pval), ]
write.csv(sorted_df,"/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_enrich_qtl_genome_name_w05.csv")


out.enrich_qtl_type <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "QTL_type",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)

sorted_df_type <- out.enrich_qtl_type[order(out.enrich_qtl_type$adj.pval), ]
write.csv(out.enrich_qtl_type,"/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_enrich_qtl_genome_type_w05.csv")


#Plots

#Name
#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_name$ID<-paste(out.enrich_qtl_name$QTL, out.enrich_qtl_name$CHR,sep="")
#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered<-out.enrich_qtl_name[which(out.enrich_qtl_name$adj.pval<0.05),]
#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered, x="ID", pval="adj.pval")


#Type
#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_type$ID<-paste(out.enrich_qtl_type$QTL, out.enrich_qtl_type$CHR,sep="")
#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered_type<-out.enrich_qtl_type[which(out.enrich_qtl_type$adj.pval<0.05),]
#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered_type, x="ID", pval="adj.pval")

QTL type My Image

QTL name by type My Image

9.4.2.1 Windows - QTL enrichment on GALLO

QTL Enrichment outcomes

Enrichment by name (enrichment analysis will be performed for each trait individually)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval	QTL_type
11	Foot angle	6	672	128	163224	0.0000169	0.0005655	Exterior
32	Rear leg placement - side view	5	430	128	163224	0.0000251	0.0005655	Exterior
6	Calving ease	12	3819	128	163224	0.0000513	0.0005773	Reproduction
31	Rear leg placement - rear view	5	491	128	163224	0.0000471	0.0005773	Exterior
28	Net merit	6	903	128	163224	0.0000863	0.0007764	Production
37	Stillbirth	7	1363	128	163224	0.0001097	0.0008227	Reproduction
9	Feet and leg conformation	5	627	128	163224	0.0001476	0.0009488	Exterior
44	Udder depth	5	695	128	163224	0.0002371	0.0013337	Exterior
30	PTA type	4	627	128	163224	0.0015793	0.0078963	Production
43	Udder attachment	4	655	128	163224	0.0018503	0.0083263	Exterior
25	Milk tridecylic acid content	3	319	128	163224	0.0021074	0.0084756	Milk
40	Teat placement - front	3	327	128	163224	0.0022602	0.0084756	Exterior
10	Fertility index	2	108	128	163224	0.0033389	0.0115577	Reproduction
12	Head width	1	6	128	163224	0.0046960	0.0150944	Production
17	Length of productive life	6	2004	128	163224	0.0051861	0.0155583	Production
33	Respiratory rate	1	9	128	163224	0.0070359	0.0197884	Health
34	Rump conformation	1	13	128	163224	0.0101471	0.0268600	Exterior
38	Strength	3	664	128	163224	0.0157176	0.0392939	Exterior
3	Body temperature	1	23	128	163224	0.0178830	0.0423545	Health
26	Muscle calcium content	1	40	128	163224	0.0308966	0.0695174	Meat and Carcass
27	Muscle sodium content	1	56	128	163224	0.0429884	0.0921180	Meat and Carcass
14	Interval from first to last insemination	2	445	128	163224	0.0481346	0.0984572	Reproduction
15	Interval to first estrus after calving	3	1053	128	163224	0.0505607	0.0989232	Reproduction
45	Udder height	2	504	128	163224	0.0599662	0.1124366	Exterior
16	Intramuscular fat	1	117	128	163224	0.0877305	0.1579149	Meat and Carcass
8	Fat thickness at the 12th rib	1	169	128	163224	0.1242283	0.2150105	Meat and Carcass
22	Milk glycosylated kappa-casein percentage	4	2527	128	163224	0.1380822	0.2301370	Milk
1	Age at first calving	1	233	128	163224	0.1671652	0.2686584	Reproduction
39	Teat length	1	300	128	163224	0.2098779	0.3148168	Exterior
41	Teat placement - rear	1	298	128	163224	0.2086349	0.3148168	Exterior
36	Somatic cell score	2	1122	128	163224	0.2199895	0.3193396	Health
18	M. paratuberculosis susceptibility	1	492	128	163224	0.3206093	0.4508568	Health
2	Body depth	1	616	128	163224	0.3837908	0.5233511	Production
19	Marbling score	2	1817	128	163224	0.4175991	0.5369131	Meat and Carcass
35	Shear force	3	2954	128	163224	0.4091307	0.5369131	Meat and Carcass
24	Milk protein yield	3	3093	128	163224	0.4380445	0.5475556	Milk
13	Inseminations per conception	1	790	128	163224	0.4627346	0.5627854	Reproduction
29	Pregnancy rate	1	944	128	163224	0.5241831	0.6207431	Reproduction
5	Bovine tuberculosis susceptibility	1	1155	128	163224	0.5972036	0.6890811	Health
4	Body weight	1	4289	128	163224	0.9669506	0.9713008	Production
7	Connective tissue amount	1	3142	128	163224	0.9170032	0.9713008	Meat and Carcass
20	Milk fat percentage	5	10941	128	163224	0.9357122	0.9713008	Milk
21	Milk fat yield	4	8220	128	163224	0.8906967	0.9713008	Milk
23	Milk protein percentage	3	8803	128	163224	0.9713008	0.9713008	Milk
42	Tenderness score	1	3483	128	163224	0.9368355	0.9713008	Meat and Carcass

My Image

Enrichment by QTL_type (enrichment processes performed for the QTL classes)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval
1	Exterior	41	9077	128	163224	0.0000000	0.0000000
2	Health	6	5889	128	163224	0.3161000	0.6049498
3	Meat and Carcass	11	18258	128	163224	0.8597556	1.0000000
4	Milk	22	75352	128	163224	1.0000000	1.0000000
5	Production	19	19640	128	163224	0.1968827	0.5906482
6	Reproduction	29	35008	128	163224	0.4032999	0.6049498

My Image

9.4.3 0.1% Windows - BLUPF90+ GALLO

gwas <- read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w10_snp_rsid_snpvar_01.csv")

colnames(gwas)
colnames(gwas) <- c("X", "CHR", "BP", "SNP", "Var")


# GALLO

#import a QTL annotation file
qtl_UCD1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Animal_QTLdb_release53_cattleARS_UCD1.gff.gz",file_type="gff")

#import a gene annotation file
gene_UDC1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Bos_taurus.ARS-UCD1.2.110.gtf.gz",file_type="gtf")

#FINDING GENES AND QTLs ARROUND THE MARKER

#FINDING GENES
out.genes <- find_genes_qtls_around_markers(db_file= gene_UDC1_2, 
                                            marker_file= gwas, 
                                            method = "gene",
                                            marker = "snp", 
                                            interval = 50000, 
                                            nThreads = NULL)

write.csv(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_genes_w01.csv")

#FINDING QTLs

out.qtl <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                          marker_file= gwas, 
                                          method = "qtl",
                                          marker = "snp", 
                                          interval = 50000, 
                                          nThreads = NULL)


write.table(out.qtl, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_qtl_w_01.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

library(tidyverse)
out.qtl.clean <- select(out.qtl, c("SNP", "CHR", "QTL_type", "start_pos", "end_pos","QTL_ID"))
write.csv(out.qtl.clean, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_qtl_w_01_clean.csv")

table(out.genes$gene_biotype)

The GALLO output are bellow:

For GENES Because this file has 1,608 rows we’ll not show here, but you can take a look in the file: /home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_genes_w01.csv

FOR QTLs Because this file has 7,347 rows we’ll not show here, but you can take a look in the file: /home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_qtl_w_01_clean.csv

QTL Plots

# Set working directory
setwd("/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10")

#QTL type plot
oldpar <- par(mar=c(1,10,1,1))
plot_qtl_info(out.qtl, qtl_plot = "qtl_type", cex=1.5)

#QTL names plot (by type)

# Set the width and height in pixels for 600 DPI
png("qtl_names_w01.png", width=5100, height=6600, res=600)  # Width and height in pixels
# Set layout for multiple plots
par(mfrow=c(6, 1), mar=c(2, 20, 1, 1))
# List of QTL classes for titles
qtl_classes <- c("Production", "Reproduction", "Milk", "Meat_and_Carcass", "Health", "Exterior")
# Loop through each QTL class and plot
for (qtl_class in qtl_classes) {
  plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class=qtl_class)
  title(main=qtl_class)  # Add the QTL class as the main title for each plot
}
# Close the device
dev.off()


#QTL enrichment analysis 

out.enrich_qtl_name <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "Name",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)


# Sorting the dataframe in ascending order of adj.pval
sorted_df <- out.enrich_qtl_name[order(out.enrich_qtl_name$adj.pval), ]
write.csv(sorted_df,"/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_enrich_qtl_genome_name_w01.csv")


out.enrich_qtl_type <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "QTL_type",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)

sorted_df_type <- out.enrich_qtl_type[order(out.enrich_qtl_type$adj.pval), ]
write.csv(out.enrich_qtl_type,"/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_enrich_qtl_genome_type_w01.csv")


#Plots

#Name
#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_name$ID<-paste(out.enrich_qtl_name$QTL, out.enrich_qtl_name$CHR,sep="")
#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered<-out.enrich_qtl_name[which(out.enrich_qtl_name$adj.pval<0.05),]
#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered, x="ID", pval="adj.pval")


#Type
#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_type$ID<-paste(out.enrich_qtl_type$QTL, out.enrich_qtl_type$CHR,sep="")
#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered_type<-out.enrich_qtl_type[which(out.enrich_qtl_type$adj.pval<0.05),]
#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered_type, x="ID", pval="adj.pval")

QTL type My Image

QTL name by type My Image

9.4.3.1 Windows - QTL enrichment on GALLO

QTL Enrichment outcomes

Enrichment by name (enrichment analysis will be performed for each trait individually)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval	QTL_type
172	Non-return rate	227	2312	4461	163224	0.0000000	0.0000000	Reproduction
126	Milk myristic acid content	131	902	4461	163224	0.0000000	0.0000000	Milk
33	Clinical mastitis	84	466	4461	163224	0.0000000	0.0000000	Health
10	Average daily gain	218	3140	4461	163224	0.0000000	0.0000000	Production
168	Myristic acid content	41	120	4461	163224	0.0000000	0.0000000	Meat and Carcass
120	Milk lauric acid content	60	366	4461	163224	0.0000000	0.0000000	Milk
173	Oleic acid content	31	101	4461	163224	0.0000000	0.0000000	Meat and Carcass
135	Milk potassium content	67	570	4461	163224	0.0000000	0.0000000	Milk
76	Length of productive life	130	2004	4461	163224	0.0000000	0.0000000	Production
96	Milk butyric acid content	71	828	4461	163224	0.0000000	0.0000000	Milk
100	Milk capric acid content	73	912	4461	163224	0.0000000	0.0000000	Milk
134	Milk phosphorylated alpha-S2-casein percentage	31	228	4461	163224	0.0000000	0.0000000	Milk
170	Myristoleic acid content	16	68	4461	163224	0.0000000	0.0000000	Meat and Carcass
175	Palmitoleic acid content	12	46	4461	163224	0.0000000	0.0000000	Meat and Carcass
174	Palmitic acid content	7	12	4461	163224	0.0000000	0.0000001	Meat and Carcass
139	Milk protein yield	138	3093	4461	163224	0.0000000	0.0000004	Milk
95	Milk beta-lactoglobulin protein content	20	177	4461	163224	0.0000001	0.0000013	Milk
9	Atherogenic index	5	8	4461	163224	0.0000008	0.0000096	Meat and Carcass
22	Bovine respiratory disease susceptibility	46	789	4461	163224	0.0000020	0.0000232	Health
73	Ketosis	30	441	4461	163224	0.0000064	0.0000695	Health
89	Metabolic body weight	154	4039	4461	163224	0.0000314	0.0003240	Production
114	Milk glycosylated kappa-casein percentage	102	2527	4461	163224	0.0000869	0.0008570	Milk
92	Milk alpha-S2-casein percentage	18	235	4461	163224	0.0000973	0.0009176	Milk
154	Monounsaturated fatty acid content	8	60	4461	163224	0.0002232	0.0020185	Meat and Carcass
47	Eye area pigmentation	7	49	4461	163224	0.0003560	0.0030899	Exterior
62	Infectious bovine keratoconjunctivitis susceptibility	5	24	4461	163224	0.0004188	0.0034953	Health
148	Milk unsaturated fatty acid content	6	38	4461	163224	0.0005409	0.0043476	Milk
187	Saturated fatty acid content	8	71	4461	163224	0.0007124	0.0055211	Meat and Carcass
155	Monounsaturated to saturated fatty acid ratio	2	2	4461	163224	0.0007468	0.0055881	Meat and Carcass
141	Milk saturated fatty acid content	8	80	4461	163224	0.0015644	0.0113159	Milk
16	Body temperature	4	23	4461	163224	0.0032549	0.0227840	Health
88	Medium-chain fatty acid content	2	6	4461	163224	0.0104106	0.0705968	Meat and Carcass
15	Body length	4	32	4461	163224	0.0108840	0.0715709	Production
36	Cooking loss	3	18	4461	163224	0.0122425	0.0775241	Meat and Carcass
196	Sperm motility	7	91	4461	163224	0.0125039	0.0775241	Reproduction
24	Calf mortality	2	7	4461	163224	0.0143119	0.0862687	Reproduction
160	Muscle creatine content	5	54	4461	163224	0.0158868	0.0931739	Meat and Carcass
48	Facial pigmentation	4	36	4461	163224	0.0163639	0.0934463	Exterior
171	Net merit	36	903	4461	163224	0.0173687	0.0966411	Production
182	Respiratory rate	2	9	4461	163224	0.0236603	0.1283573	Health
57	General health index	1	1	4461	163224	0.0273305	0.1289288	Health
85	Marfan syndrome-like disease	1	1	4461	163224	0.0273305	0.1289288	Health
102	Milk caprylic acid content	32	811	4461	163224	0.0268019	0.1289288	Milk
195	Sperm head abnormalities	1	1	4461	163224	0.0273305	0.1289288	Reproduction
197	Sperm tail abnormalities	1	1	4461	163224	0.0273305	0.1289288	Reproduction
215	Unsaturated fatty acid content	1	1	4461	163224	0.0273305	0.1289288	Meat and Carcass
54	Fertility index	7	108	4461	163224	0.0290780	0.1342538	Reproduction
6	Alkaline phosphatase level	2	11	4461	163224	0.0348657	0.1544055	Health
199	Stayability	2	11	4461	163224	0.0348657	0.1544055	Reproduction
213	Udder height	21	504	4461	163224	0.0393172	0.1706368	Exterior
110	Milk fat content	6	92	4461	163224	0.0406206	0.1728366	Milk
142	Milk saturated to unsaturated fatty acid ratio	3	29	4461	163224	0.0439516	0.1834135	Milk
177	Pregnancy rate	35	944	4461	163224	0.0456939	0.1870863	Reproduction
64	Inseminations per conception	30	790	4461	163224	0.0471343	0.1894099	Reproduction
113	Milk fever	1	2	4461	163224	0.0539143	0.2089178	Health
169	Myristic and palmitic acid ratio	1	2	4461	163224	0.0539143	0.2089178	Meat and Carcass
146	Milk tridecylic acid content	14	319	4461	163224	0.0577579	0.2198854	Milk
78	Liver abscess	3	33	4461	163224	0.0606176	0.2267933	Health
178	PTA type	24	627	4461	163224	0.0646760	0.2360992	Production
200	Stillbirth	47	1363	4461	163224	0.0652809	0.2360992	Reproduction
212	Udder depth	26	695	4461	163224	0.0697443	0.2481067	Exterior
34	Conception rate	43	1255	4461	163224	0.0805665	0.2775068	Reproduction
106	Milk cis-12-Octadecenoic acid content	1	3	4461	163224	0.0797716	0.2775068	Milk
7	Angularity	3	38	4461	163224	0.0849337	0.2843841	Exterior
180	Rear leg placement - side view	17	430	4461	163224	0.0851842	0.2843841	Exterior
209	Udder attachment	24	655	4461	163224	0.0934154	0.3071384	Exterior
53	Feet and leg conformation	23	627	4461	163224	0.0978354	0.3168699	Exterior
18	Body weight gain	45	1354	4461	163224	0.1071471	0.3260573	Production
52	Feed intake	4	66	4461	163224	0.1065880	0.3260573	Production
83	Male fertility	2	20	4461	163224	0.1026069	0.3260573	Reproduction
122	Milk long-chain fatty acid content	1	4	4461	163224	0.1049224	0.3260573	Milk
211	Udder cleft	18	477	4461	163224	0.1081849	0.3260573	Exterior
68	Interdigital hyperplasia	5	93	4461	163224	0.1116235	0.3273285	Exterior
191	Sole ulcer	5	93	4461	163224	0.1116235	0.3273285	Exterior
50	Fecal larva count	6	125	4461	163224	0.1286352	0.3472694	Health
51	Feed conversion ratio	2	22	4461	163224	0.1204428	0.3472694	Production
79	Long-chain fatty acid content	1	5	4461	163224	0.1293860	0.3472694	Meat and Carcass
108	Milk delta-9-desaturase content	1	5	4461	163224	0.1293860	0.3472694	Milk
153	Monocyte number	1	5	4461	163224	0.1293860	0.3472694	Health
189	Semen volume	2	23	4461	163224	0.1296259	0.3472694	Reproduction
210	Udder balance	1	5	4461	163224	0.1293860	0.3472694	Exterior
5	Age at second calving	1	6	4461	163224	0.1531811	0.3865150	Reproduction
19	Bone quality	2	25	4461	163224	0.1484422	0.3865150	Exterior
59	Head width	1	6	4461	163224	0.1531811	0.3865150	Production
132	Milk phosphocholine level	1	6	4461	163224	0.1531811	0.3865150	Milk
183	Retained placenta	1	6	4461	163224	0.1531811	0.3865150	Reproduction
8	Anti-Müllerian hormone level	2	26	4461	163224	0.1580454	0.3899729	Health
138	Milk protein percentage	256	8803	4461	163224	0.1581457	0.3899729	Milk
56	Foot angle	23	672	4461	163224	0.1627126	0.3967261	Exterior
152	Milking speed	33	1011	4461	163224	0.1719461	0.4145811	Milk
45	Duration of inactivity during novel object test	1	7	4461	163224	0.1763259	0.4188389	Exterior
145	Milk tricosanoic acid content	2	28	4461	163224	0.1775723	0.4188389	Milk
193	Somatic cell score	36	1122	4461	163224	0.1853615	0.4325102	Health
29	Carcass weight	62	2020	4461	163224	0.1922266	0.4406949	Meat and Carcass
179	Rear leg placement - rear view	17	491	4461	163224	0.1929309	0.4406949	Exterior
123	Milk margaric acid content	3	59	4461	163224	0.2184358	0.4937558	Milk
65	Insulin level	1	9	4461	163224	0.2207356	0.4938106	Health
37	Curd firmness	4	89	4461	163224	0.2263612	0.4990652	Milk
194	sperm counts	2	33	4461	163224	0.2276841	0.4990652	Reproduction
2	Adrenocorticotropic hormone level	2	34	4461	163224	0.2378405	0.5002046	Health
11	Biceps brachii weight	1	10	4461	163224	0.2420345	0.5002046	Meat and Carcass
67	Interdigital dermatitis	1	10	4461	163224	0.2420345	0.5002046	Exterior
128	Milk oleic acid content	34	1089	4461	163224	0.2382404	0.5002046	Milk
140	Milk riboflavin content	17	509	4461	163224	0.2336729	0.5002046	Milk
214	Udder width	1	10	4461	163224	0.2420345	0.5002046	Exterior
40	Dairy form	17	514	4461	163224	0.2455918	0.5027680	Exterior
60	Hip width	2	36	4461	163224	0.2582100	0.5188108	Production
181	Residual feed intake	14	418	4461	163224	0.2570187	0.5188108	Production
186	Rump width	17	526	4461	163224	0.2751345	0.5477448	Production
87	Mean corpuscular hemoglobin concentration	1	12	4461	163224	0.2829021	0.5481229	Health
201	Strength	21	664	4461	163224	0.2787307	0.5481229	Exterior
208	Time to curd firmness	1	12	4461	163224	0.2829021	0.5481229	Milk
149	Milk vitamin B-12 content	2	39	4461	163224	0.2887812	0.5545622	Milk
158	Muscle calcium content	2	40	4461	163224	0.2989460	0.5657887	Meat and Carcass
205	Teat placement - rear	10	298	4461	163224	0.2998420	0.5657887	Exterior
185	Rump conformation	1	13	4461	163224	0.3025022	0.5658878	Exterior
31	Cheese protein recovery	2	42	4461	163224	0.3191965	0.5820642	Milk
162	Muscle magnesium content	2	42	4461	163224	0.3191965	0.5820642	Meat and Carcass
217	Yield grade	2	42	4461	163224	0.3191965	0.5820642	Meat and Carcass
118	Milk lactose content	4	105	4461	163224	0.3231725	0.5844036	Milk
46	Ear size	2	44	4461	163224	0.3393017	0.6079144	Exterior
157	Muscle anserine content	4	108	4461	163224	0.3417767	0.6079144	Meat and Carcass
12	Birth index	1	17	4461	163224	0.3756910	0.6521996	Reproduction
43	Docosahexaenoic acid content	1	17	4461	163224	0.3756910	0.6521996	Meat and Carcass
55	First service conception	16	527	4461	163224	0.3700692	0.6521996	Reproduction
39	Curd yield	1	18	4461	163224	0.3927555	0.6764122	Milk
72	Intramuscular fat	4	117	4461	163224	0.3975766	0.6778472	Meat and Carcass
198	Stature	27	929	4461	163224	0.3998361	0.6778472	Exterior
203	Teat length	9	300	4461	163224	0.4356308	0.7328053	Exterior
32	Chest width	1	22	4461	163224	0.4564758	0.7337426	Production
74	Lactation persistency	7	231	4461	163224	0.4446153	0.7337426	Milk
90	Methane production	3	90	4461	163224	0.4474954	0.7337426	Production
117	Milk lactoferrin content	1	22	4461	163224	0.4564758	0.7337426	Milk
124	Milk mid-infrared spectra	2	55	4461	163224	0.4455939	0.7337426	Milk
165	Muscle sodium content	2	56	4461	163224	0.4547957	0.7337426	Meat and Carcass
14	Body height	5	166	4461	163224	0.4763337	0.7532300	Production
17	Body weight	118	4289	4461	163224	0.4837775	0.7532300	Production
28	Cannon bone circumference	1	25	4461	163224	0.4998393	0.7532300	Production
38	Curd solids yield	1	25	4461	163224	0.4998393	0.7532300	Milk
103	Milk casein index	1	24	4461	163224	0.4857834	0.7532300	Milk
136	Milk profitability index	1	25	4461	163224	0.4998393	0.7532300	Milk
161	Muscle iron content	2	60	4461	163224	0.4906960	0.7532300	Meat and Carcass
176	Perinatal mortality	1	24	4461	163224	0.4857834	0.7532300	Reproduction
192	Somatic cell count	4	132	4461	163224	0.4882405	0.7532300	Health
13	Body depth	17	616	4461	163224	0.5176377	0.7746716	Production
71	Intestinal atresia	2	65	4461	163224	0.5334108	0.7860699	Health
75	Lean meat yield	17	621	4461	163224	0.5310670	0.7860699	Meat and Carcass
109	Milk eicosapentaenoic acid content	1	28	4461	163224	0.5397439	0.7860699	Milk
204	Teat placement - front	9	327	4461	163224	0.5378775	0.7860699	Exterior
105	Milk cholesterol content	1	29	4461	163224	0.5523251	0.7990303	Milk
125	Milk monounsaturated fatty acid content	1	32	4461	163224	0.5880430	0.8450684	Milk
26	Calf sucking reflex	1	33	4461	163224	0.5993042	0.8555856	Exterior
151	Milk zinc content	10	384	4461	163224	0.6049225	0.8579619	Milk
119	Milk lactose yield	1	35	4461	163224	0.6209118	0.8637042	Milk
137	Milk protein content	1	35	4461	163224	0.6209118	0.8637042	Milk
163	Muscle phosphorus content	1	35	4461	163224	0.6209118	0.8637042	Meat and Carcass
30	Cheese fat recovery	1	36	4461	163224	0.6312747	0.8725261	Milk
1	Abomasum displacement	2	85	4461	163224	0.6786822	0.9321141	Health
116	Milk kappa-casein percentage	118	4499	4461	163224	0.6900415	0.9358687	Milk
167	Muscle zinc content	1	42	4461	163224	0.6877680	0.9358687	Meat and Carcass
77	Lifetime profit index	1	49	4461	163224	0.7428356	0.9950329	Production
156	Multiple birth	2	96	4461	163224	0.7415251	0.9950329	Reproduction
41	Digital cushion thickness	1	51	4461	163224	0.7567046	0.9951812	Exterior
164	Muscle potassium content	1	50	4461	163224	0.7498662	0.9951812	Meat and Carcass
216	White line disease	1	51	4461	163224	0.7567046	0.9951812	Exterior
3	Age at first calving	4	233	4461	163224	0.8823354	1.0000000	Reproduction
4	Age at puberty	5	8222	4461	163224	1.0000000	1.0000000	Reproduction
20	Bone weight	3	402	4461	163224	0.9989077	1.0000000	Meat and Carcass
21	Bovine coronavirus susceptibility	1	73	4461	163224	0.8677886	1.0000000	Health
23	Bovine tuberculosis susceptibility	14	1155	4461	163224	0.9998691	1.0000000	Health
25	Calf size	1	69	4461	163224	0.8522835	1.0000000	Reproduction
27	Calving ease	70	3819	4461	163224	0.9998937	1.0000000	Reproduction
35	Connective tissue amount	66	3142	4461	163224	0.9900416	1.0000000	Meat and Carcass
42	Digital dermatitis	2	110	4461	163224	0.8060144	1.0000000	Exterior
44	Dry matter intake	15	2186	4461	163224	1.0000000	1.0000000	Production
49	Fat thickness at the 12th rib	2	169	4461	163224	0.9469097	1.0000000	Meat and Carcass
58	Gestation length	10	636	4461	163224	0.9800620	1.0000000	Reproduction
61	Hoof and leg disorders	1	60	4461	163224	0.8104235	1.0000000	Exterior
63	Inhibin level	4	293	4461	163224	0.9599126	1.0000000	Reproduction
66	Insulin-like growth factor 1 level	2	129	4461	163224	0.8705023	1.0000000	Health
69	Interval from first to last insemination	6	445	4461	163224	0.9828601	1.0000000	Reproduction
70	Interval to first estrus after calving	20	1053	4461	163224	0.9666930	1.0000000	Reproduction
80	Longissimus muscle area	33	1420	4461	163224	0.8494685	1.0000000	Meat and Carcass
81	Luteal activity	14	1306	4461	163224	0.9999909	1.0000000	Reproduction
82	M. paratuberculosis susceptibility	11	492	4461	163224	0.7887653	1.0000000	Health
84	Marbling score	31	1817	4461	163224	0.9984368	1.0000000	Meat and Carcass
86	Maturity rate	2	243	4461	163224	0.9907192	1.0000000	Production
91	Milk alpha-S1-casein percentage	1	65	4461	163224	0.8349606	1.0000000	Milk
93	Milk arachidic acid content	1	60	4461	163224	0.8104235	1.0000000	Milk
94	Milk beta-lactoglobulin percentage	4	291	4461	163224	0.9583769	1.0000000	Milk
97	Milk C14 index	51	4437	4461	163224	1.0000000	1.0000000	Milk
98	Milk C16 index	36	2002	4461	163224	0.9974754	1.0000000	Milk
99	Milk C18 index	1	246	4461	163224	0.9989106	1.0000000	Milk
101	Milk caproic acid content	12	675	4461	163224	0.9575803	1.0000000	Milk
104	Milk casein percentage	4	259	4461	163224	0.9251845	1.0000000	Milk
107	Milk conjugated linoleic acid content	4	274	4461	163224	0.9429551	1.0000000	Milk
111	Milk fat percentage	138	10941	4461	163224	1.0000000	1.0000000	Milk
112	Milk fat yield	147	8220	4461	163224	1.0000000	1.0000000	Milk
115	Milk iron content	2	217	4461	163224	0.9826892	1.0000000	Milk
121	Milk linoleic acid content	5	857	4461	163224	0.9999992	1.0000000	Milk
127	Milk myristoleic acid content	38	3047	4461	163224	1.0000000	1.0000000	Milk
129	Milk palmitic acid content	12	1711	4461	163224	1.0000000	1.0000000	Milk
130	Milk palmitoleic acid content	22	2422	4461	163224	1.0000000	1.0000000	Milk
131	Milk pentadecylic acid content	6	280	4461	163224	0.7788727	1.0000000	Milk
133	Milk phosphorus content	2	154	4461	163224	0.9254224	1.0000000	Milk
143	Milk stearic acid content	3	218	4461	163224	0.9387352	1.0000000	Milk
144	Milk tetracosanoic acid content	2	105	4461	163224	0.7848243	1.0000000	Milk
147	Milk unglycosylated kappa-casein percentage	50	2351	4461	163224	0.9735648	1.0000000	Milk
150	Milk yield	165	6432	4461	163224	0.8101634	1.0000000	Milk
159	Muscle carnosine content	1	67	4461	163224	0.8438621	1.0000000	Meat and Carcass
166	Muscle total collagen content	1	68	4461	163224	0.8481312	1.0000000	Meat and Carcass
184	Rump angle	3	179	4461	163224	0.8696818	1.0000000	Exterior
188	Scrotal circumference	15	8064	4461	163224	1.0000000	1.0000000	Reproduction
190	Shear force	73	2954	4461	163224	0.8253604	1.0000000	Meat and Carcass
202	Subcutaneous fat thickness	3	331	4461	163224	0.9944835	1.0000000	Meat and Carcass
206	Tenderness score	45	3483	4461	163224	1.0000000	1.0000000	Meat and Carcass
207	Tick resistance	1	89	4461	163224	0.9151568	1.0000000	Health

My Image

Enrichment by QTL_type (enrichment processes performed for the QTL classes)

X	QTL	N_QTLs	N_QTLs_db	Total_annotated_QTLs	Total_QTLs_db	pvalue	adj.pval
1	Exterior	304	9077	4461	163224	0.0001814	0.0003628
2	Health	265	5889	4461	163224	0.0000000	0.0000000
3	Meat and Carcass	503	18258	4461	163224	0.4308370	0.6462554
4	Milk	1996	75352	4461	163224	0.9742520	1.0000000
5	Production	814	19640	4461	163224	0.0000000	0.0000000
6	Reproduction	579	35008	4461	163224	1.0000000	1.0000000

My Image

9.5 Comparison among ssGWAS for independent SNPs against Window 0.5% and window 0.1%

9.5.1 SNPs

pvalue <- read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/gwas_ind_seg_sig_SNPname_rsID.csv")
w05 <- read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w10_snp_rsid_snpvar_05.csv")
w01 <- read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w10_snp_rsid_snpvar_01.csv")



intersecpt_pv_w05_name <- intersect(pvalue$SNP_name, w05$SNP_name)
intersecpt_pv_w01_name <- intersect(pvalue$SNP_name, w01$SNP_ID)


# Create the matrix
common_ids <- matrix(NA, nrow = length(intersecpt_pv_w01_name), ncol = 3)
common_ids[, 1] <- intersecpt_pv_w01_name  # First column
common_ids[, 2] <- ifelse(intersecpt_pv_w01_name %in% intersecpt_pv_w01_name, "YES", NA)
common_ids[, 3] <- ifelse(intersecpt_pv_w01_name %in% intersecpt_pv_w05_name, "YES", NA)
common_ids <- as.data.frame(common_ids)
colnames(common_ids) <- c("P_value_SNPs", "W_0.1%_SNPs", "W_0.5%_SNPs")
common_ids$rsID <- pvalue$rsID[match(common_ids$P_value_SNPs, pvalue$SNP_name)]
common_ids <- common_ids[, c("P_value_SNPs", "rsID", "W_0.1%_SNPs", "W_0.5%_SNPs")]

write_csv(common_ids, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/common_SNPs.csv")

Bellow are the SNPs which had the p-value bellow bonferroni and also apeeared in the set of SNPs which explain more thant 0.5% or 0.1% of genetic variance.

P_value_SNPs	rsID	W_0.1._SNPs	W_0.5._SNPs
ARS-BFGL-NGS-25298	rs109868537	YES	YES
ARS-BFGL-NGS-37809	rs42751504	YES	YES
ARS-BFGL-NGS-74948	rs41585925	YES	NA
ARS-BFGL-NGS-82859	rs110506037	YES	NA
BTB-01641394	rs42752353	YES	YES

9.5.2 Genes

pvalue <- read.csv("/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_genes_50k_pvalue.csv")
w05 <- read.csv("/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_genes_w_05.csv")
w01 <- read.csv("/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_genes_w01.csv")

intersecpt_pv_w05_name <- intersect(pvalue$gene_name, w05$gene_name)
intersecpt_pv_w05_ID <- intersect(pvalue$gene_id, w05$gene_id)

intersecpt_pv_w01_name <- intersect(pvalue$gene_name, w01$gene_name)
intersecpt_pv_w01_ID <- intersect(pvalue$gene_id, w01$gene_id)

# Create the matrix
common_ids <- matrix(NA, nrow = length(intersecpt_pv_w01_ID), ncol = 3)
common_ids[, 1] <- intersecpt_pv_w01_ID  # First column
common_ids[, 2] <- ifelse(intersecpt_pv_w01_ID %in% intersecpt_pv_w01_ID, "YES", NA)
common_ids[, 3] <- ifelse(intersecpt_pv_w01_ID %in% intersecpt_pv_w05_ID, "YES", NA)
common_ids <- as.data.frame(common_ids)
colnames(common_ids) <- c("P_value_id", "W_0.1%", "W_0.5%")
common_ids$P_value_name <- pvalue$gene_name[match(common_ids$P_value_id, pvalue$gene_id)]
common_ids <- common_ids[, c("P_value_id", "P_value_name", "W_0.1%", "W_0.5%")]
common_ids$gene_biotype <- pvalue$gene_biotype[match(common_ids$P_value_id, pvalue$gene_id)]

write_csv(common_ids, "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/common_genes.csv")

Bellow we can find the Genes that were found close (50kb) to SNPs that were:

Individually significant (P_value)
Explain more than 0.5% additive genetic variance
Explain more than 0.1% additive genetic variance

P_value_id	P_value_name	W_0.1.	W_0.5.	gene_biotype
ENSBTAG00000002950	TMEM132D	YES	NA	protein_coding
ENSBTAG00000020798	C3H1orf94	YES	NA	protein_coding
ENSBTAG00000005784	CSMD2	YES	YES	protein_coding
ENSBTAG00000013537	FER1L6	YES	NA	protein_coding
ENSBTAG00000048373	NA	YES	NA	protein_coding
ENSBTAG00000030718	SPATA3	YES	NA	protein_coding
ENSBTAG00000054626	NA	YES	NA	lncRNA

9.5.3 QTLs

# GALLO

#import a QTL annotation file
qtl_UCD1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Animal_QTLdb_release53_cattleARS_UCD1.gff.gz",file_type="gff")


#import MARKER files = the GWAS output
gwas_p = read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/gwas_ind_seg_sig_SNPname_rsID.csv")
colnames(gwas_p) <- c("X", "SNP", "rsID", "CHR", "BP", "LOG_P")

pvalue <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                         marker_file= gwas_p, 
                                         method = "qtl",
                                         marker = "snp", 
                                         interval = 50000, 
                                         nThreads = NULL)

gwas_w05 <- read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w10_snp_rsid_snpvar_05.csv")
colnames(gwas_w05) <- c("X", "SNP", "rsID", "CHR", "BP", "Var")

w05 <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                         marker_file= gwas_w05, 
                                         method = "qtl",
                                         marker = "snp", 
                                         interval = 50000, 
                                         nThreads = NULL)

gwas_w01 <- read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w10_snp_rsid_snpvar_01.csv")
colnames(gwas_w01)
colnames(gwas_w01) <- c("X", "CHR", "BP", "SNP", "Var")
gwas_w01 <- gwas_w01[, c("X","SNP", "CHR", "BP", "Var")]

w01 <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                          marker_file= gwas_w01, 
                                          method = "qtl",
                                          marker = "snp", 
                                          interval = 50000, 
                                          nThreads = NULL)



#################################################################

intersecpt_pv_w05_ID <- intersect(pvalue$QTL_ID, w05$QTL_ID)

intersecpt_pv_w01_ID <- intersect(pvalue$QTL_ID, w01$QTL_ID)

# Create the matrix
common_ids <- matrix(NA, nrow = length(intersecpt_pv_w01_ID), ncol = 3)
common_ids[, 1] <- intersecpt_pv_w01_ID  # First column
common_ids[, 2] <- ifelse(intersecpt_pv_w01_ID %in% intersecpt_pv_w01_ID, "YES", NA)
common_ids[, 3] <- ifelse(intersecpt_pv_w01_ID %in% intersecpt_pv_w05_ID, "YES", NA)
common_ids <- as.data.frame(common_ids)
colnames(common_ids) <- c("P_value_id", "W_0.1%", "W_0.5%")
common_ids$P_value_name <- pvalue$Name[match(common_ids$P_value_id, pvalue$QTL_ID)]
common_ids <- common_ids[, c("P_value_id", "P_value_name", "W_0.1%", "W_0.5%")]
common_ids$QTL_type <- pvalue$QTL_type[match(common_ids$P_value_id, pvalue$QTL_ID)]

write_csv(common_ids, "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/common_QTLs.csv")

Bellow we can find QTLs that were found close (50kb) to SNPs that were:

Individually significant (P_value)
Explain more than 0.5% additive genetic variance
Explain more than 0.1% additive genetic variance

P_value_id	W_0.1.	W_0.5.	P_value_name	QTL_type
137202	YES	NA	Bovine respiratory disease susceptibility	Health
170274	YES	NA	Fecal larva count	Health
170275	YES	NA	Fecal larva count	Health
170276	YES	NA	Fecal larva count	Health
30008	YES	YES	Interval to first estrus after calving	Reproduction
30244	YES	YES	Interval to first estrus after calving	Reproduction
152258	YES	YES	Muscle sodium content	Meat_and_Carcass
225527	YES	YES	Shear force	Meat_and_Carcass
25606	YES	NA	Milk yield	Milk
26355	YES	NA	Milk protein yield	Milk
36989	YES	NA	Lean meat yield	Meat_and_Carcass
104606	YES	NA	Milk fat percentage	Milk
104607	YES	NA	Milk fat percentage	Milk
220989	YES	NA	Interdigital hyperplasia	Exterior
122755	YES	NA	Length of productive life	Production
123539	YES	NA	Length of productive life	Production
122756	YES	NA	Length of productive life	Production
123540	YES	NA	Length of productive life	Production
122757	YES	NA	Length of productive life	Production
123541	YES	NA	Length of productive life	Production
122758	YES	NA	Length of productive life	Production
123542	YES	NA	Length of productive life	Production
122759	YES	NA	Length of productive life	Production
123543	YES	NA	Length of productive life	Production
122760	YES	NA	Length of productive life	Production
123544	YES	NA	Length of productive life	Production
200902	YES	NA	Milk pentadecylic acid content	Milk
202500	YES	NA	Milk pentadecylic acid content	Milk
202905	YES	NA	Milk pentadecylic acid content	Milk
201638	YES	NA	Milk pentadecylic acid content	Milk
202885	YES	NA	Milk pentadecylic acid content	Milk
39599	YES	NA	Calving ease	Reproduction
39600	YES	NA	Pregnancy rate	Reproduction
39601	YES	NA	Stillbirth	Reproduction
39602	YES	NA	Foot angle	Exterior
39603	YES	NA	Feet and leg conformation	Exterior
39604	YES	NA	Milk fat percentage	Milk
39605	YES	NA	PTA type	Production
39606	YES	NA	Udder attachment	Exterior
39607	YES	NA	Milk fat yield	Milk
39608	YES	NA	Net merit	Production
39609	YES	NA	Length of productive life	Production
39610	YES	NA	Rear leg placement - side view	Exterior
39611	YES	NA	Udder height	Exterior
39612	YES	NA	Rump width	Production
39613	YES	NA	Calving ease	Reproduction
39614	YES	NA	Somatic cell score	Health
39615	YES	NA	Strength	Exterior
39616	YES	NA	Udder depth	Exterior
125235	YES	NA	Multiple birth	Reproduction

9.6 Allele frequency for GWAS output

I created the code bellow to find out the allele frequency for those most significant SNPs considering the two different methodologies, the “regular”ssGWAS and the window ssGWAS.

# Bringing SNP_ID to SNP_Frequency
snpmap <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/snpmap.txt_clean", header=T)
snpfreq <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/freqdata.count")
map_freq_var <- cbind(snpmap, snpfreq,snpWvar)
colnames(map_freq_var) <- c("CHR", "POS", "SNP_ID", "SNP_ORDER", "FREQ", "V1", "V2", "Var", "snp", "chr", "pos")
map_freq_var <- map_freq_var[,c("CHR", "POS", "SNP_ORDER", "SNP_ID", "FREQ", "Var") ]
map_freq_var_w05 <- filter(map_freq_var, Var > 0.5)
map_freq_var_w01 <- filter(map_freq_var, Var > 0.1)
write.csv(map_freq_var_w05, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w05_allele_freq.csv")
write.csv(map_freq_var_w01, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w01_allele_freq.csv")



# Output for significant p-value

#Estimating Bonferroni for genome independent segments
Genome_Assembly_ARS_UCD_1_2 <- read_tsv("/home/bambrozi/2_CORTISOL/GWAS/sequence_report_ARS-UCD1_2.tsv")

library(dplyr)
# Filter the rows and sum the Seq length column
# Assuming your data frame is named Genome_Assembly_ARS_UCD_1_2
L <- Genome_Assembly_ARS_UCD_1_2 %>%
  filter(`UCSC style name` %in% paste0("chr", 1:29)) %>%
  summarise(total_length = sum(`Seq length`)) %>%
  pull(total_length)
# Converting bases to Morgan (1Mb = 1cM (0,01 Morgan))
L_M <- L/10^8
# The Ne measure is based on the article bellow:
Ne <- 66 #(Makanjoula et al., 2020)
NeL <- Ne*L_M
# This is the number of independent segment in the genome.
Me <- (2*NeL)/log10(NeL)
# Calculate Bonferroni threshold (already done)
bonf <- -log10(0.05 / Me)

library(tidyverse)
#import MARKER files = the GWAS output
snpPval = read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/chrsnp_pval")
colnames(snpPval) <- c("V1", "V2", "LOG_P", "SNP", "CHR", "BP")
map_freq_logp <- cbind(snpmap, snpfreq,snpPval)
map_freq_logp <- map_freq_logp[, c("CHR", "POS", "SNP", "SNP_ID", "V2", "LOG_P")]
colnames(map_freq_logp) <- c("CHR", "POS", "SNP", "SNP_ID", "FREQ", "LOG_P")
map_freq_logp <- filter(map_freq_logp, LOG_P >= bonf)
write.csv(map_freq_logp, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/pval_allele_freq.csv")


common_snp <- read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/common_SNPs.csv")
common_snp$allele_freq <- map_freq_logp$FREQ[match(common_snp$P_value_SNPs, map_freq_logp$SNP_ID)]
write.csv(map_freq_logp, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/common_SNPs_FREQ.csv")

9.6.1 “Regular” ssGWAS

X	CHR	POS	SNP	SNP_ID	FREQ	LOG_P
1	2	41602429	3497	BTB-00096979	0.746479	4.497977
2	2	41670655	3498	Hapmap48777-BTA-47434	0.760563	4.377284
3	2	118820218	4632	Hapmap53065-rs29026778	0.535211	4.420962
4	3	110866523	6862	ARS-BFGL-NGS-118207	0.422535	4.815943
5	3	111526170	6873	ARS-BFGL-NGS-74948	0.535211	4.760351
6	3	111730561	6880	BTB-01641394	0.507042	4.390707
7	3	111751663	6881	ARS-BFGL-NGS-37809	0.507042	4.390707
8	3	111772736	6882	ARS-BFGL-NGS-25298	0.612676	5.088738
9	4	7873471	7184	Hapmap60503-rs29018741	0.211268	4.610767
10	4	24239851	7471	Hapmap60681-rs29013301	0.063380	4.641241
11	4	25933587	7502	Hapmap50554-BTA-107048	0.126761	4.352451
12	4	26511448	7515	Hapmap59011-rs29027498	0.415493	4.537662
13	4	27021431	7524	Hapmap59743-rs29017061	0.246479	5.045447
14	4	27094376	7526	BTB-00170785	0.119718	5.485063
15	4	50755462	7910	ARS-BFGL-NGS-12139	0.049296	5.015905
16	4	95650788	8704	Hapmap58854-rs29023486	0.091549	4.825103
17	4	103979600	8831	ARS-BFGL-NGS-110705	0.683099	5.591006
18	4	107787202	8888	ARS-BFGL-NGS-45265	0.661972	5.375628
19	4	109568557	8904	Hapmap48062-BTA-72409	0.345070	4.709882
20	4	110053134	8911	UA-IFASA-2147	0.542254	5.245598
21	6	36184467	11511	Hapmap23854-BTC-062412	0.232394	4.758110
22	7	110306791	14772	BTB-01148543	0.528169	4.609691
23	11	19779915	20558	Hapmap55558-rs29013980	0.352113	4.828190
24	12	17477322	22252	BTB-00488482	0.161972	4.417576
25	14	15929822	25167	ARS-BFGL-NGS-82859	0.598592	5.467439
26	15	33066384	26782	BTB-00594449	0.105634	5.041271
27	15	57930281	27199	ARS-BFGL-NGS-30515	0.154930	4.514377
28	17	46487068	29742	ARS-BFGL-NGS-12510	0.154930	4.547766
29	17	47884497	29772	ARS-BFGL-NGS-87412	0.753521	6.622354
30	17	48800954	29784	ARS-BFGL-NGS-112149	0.154930	5.268940
31	20	60365668	33553	BTB-01341053	0.732394	4.603817
32	20	65351653	33665	ARS-BFGL-NGS-91119	0.084507	4.891206
33	21	4055731	33836	ARS-BFGL-NGS-112210	0.922535	4.493938
34	23	44822126	36688	ARS-BFGL-NGS-115605	0.802817	4.316434
35	24	857728	36876	Hapmap47669-BTA-59022	0.669014	4.458018
36	28	20016672	40795	ARS-BFGL-NGS-116552	0.739437	5.130557
37	28	35807366	41057	ARS-BFGL-NGS-71077	0.514085	4.695875
38	29	33039863	41785	ARS-BFGL-NGS-41631	0.859155	4.933140

9.6.2 Windows ssGWAS

9.6.2.1 0.5%

X	CHR	POS	SNP_ORDER	SNP_ID	FREQ	Var
1	2	115622067	4582	Hapmap41888-BTA-49091	0.232394	0.8268392
2	2	115665427	4583	ARS-BFGL-BAC-35548	0.542254	1.2851806
3	2	115695003	4584	BTA-49096-no-rs	0.760563	1.6071883
4	2	115730530	4585	ARS-BFGL-NGS-30337	0.521127	1.3488469
5	2	115821065	4586	ARS-BFGL-NGS-103753	0.612676	1.1689686
6	2	115875702	4587	Hapmap54770-rs29009608	0.535211	0.7398417
7	2	115986085	4588	ARS-BFGL-NGS-43721	0.605634	0.8178265
8	2	116018639	4589	ARS-BFGL-NGS-107330	0.366197	0.5958551
9	3	111677167	6878	BTB-01948148	0.035211	0.7467347
10	3	111708236	6879	Hapmap42062-BTA-109789	0.260563	1.2415855
11	3	111730561	6880	BTB-01641394	0.507042	0.9480214
12	3	111751663	6881	ARS-BFGL-NGS-37809	0.507042	1.0607739
13	3	111772736	6882	ARS-BFGL-NGS-25298	0.612676	1.2101267
14	3	111806406	6883	ARS-BFGL-NGS-44131	0.176056	1.0252690
15	3	111833768	6884	ARS-BFGL-NGS-6202	0.302817	1.1671762
16	3	111933069	6885	ARS-BFGL-NGS-85333	0.211268	0.6077153
17	3	111965305	6886	ARS-BFGL-NGS-97849	0.373239	0.7547885
18	5	6312610	9246	BTA-73915-no-rs	0.267606	0.5103844
19	10	50554831	19228	BTB-01434227	0.591549	0.6660810
20	13	18266073	23616	Hapmap50266-BTA-13664	0.239437	0.6006650
21	13	18386942	23617	Hapmap49833-BTA-103929	0.823944	0.5282867
22	13	18509812	23619	BTA-25900-no-rs	0.732394	0.6364128
23	13	18558540	23620	ARS-BFGL-BAC-7444	0.619718	0.6630789
24	15	10879514	26382	BTB-01813405	0.253521	0.5556342
25	15	10906064	26383	BTB-02063964	0.253521	0.8788211
26	15	10936002	26384	BTB-01830390	0.352113	1.0480277
27	15	10974997	26385	BTB-01608944	0.845070	1.1206945
28	15	11100934	26386	BTA-91816-no-rs	0.267606	0.9739683
29	15	11144666	26387	BTB-01421844	0.732394	0.7131750
30	15	11185546	26388	BTB-01421892	0.732394	0.6463689
31	15	11207429	26389	BTB-01421934	0.711268	0.8898567
32	15	11236303	26390	BTB-01422008	0.711268	0.6832306
33	15	34054485	26800	ARS-BFGL-NGS-2713	0.816901	0.5549892
34	15	34109962	26801	ARS-BFGL-NGS-98724	0.598592	0.6252263
35	15	34144843	26802	ARS-BFGL-NGS-5976	0.478873	0.5490537
36	20	12087403	32778	ARS-BFGL-NGS-3276	0.352113	0.5722138
37	20	12111883	32779	ARS-BFGL-NGS-78615	0.295775	0.5689079
38	24	28417928	37341	ARS-BFGL-NGS-5141	0.204225	0.5945940
39	24	28487771	37343	ARS-BFGL-BAC-28665	0.873239	0.6899732
40	24	28516684	37344	Hapmap54981-rs29019846	0.816901	0.7747524
41	24	28540641	37345	BTB-01485274	0.591549	0.8983644
42	24	28570245	37346	Hapmap58887-rs29013502	0.647887	0.7619213
43	24	28604672	37347	BTB-01646599	0.591549	0.5894579

9.6.2.2 0.1%

As this table has 1,089 rows we are not showing here, but we are going to show bellow the allele frequency of the common SNPs among p-value, Windows 0.5% and Windows 0.1% approach:

X	P_value_SNPs	rsID	W_0.1._SNPs	W_0.5._SNPs	allele_freq
1	ARS-BFGL-NGS-25298	rs109868537	YES	YES	0.612676
2	ARS-BFGL-NGS-37809	rs42751504	YES	YES	0.507042
3	ARS-BFGL-NGS-74948	rs41585925	YES	NA	0.535211
4	ARS-BFGL-NGS-82859	rs110506037	YES	NA	0.598592
5	BTB-01641394	rs42752353	YES	YES	0.507042

10 Genetic Correlation

To assess the correlation between Cortisol phenotypes and Genomic Estimated Breeding Values (GEBVs), we opt for a linear regression instead of a standard correlation test. This decision is driven by the non-normal distribution of our Cortisol phenotypes, which violates the assumptions required for traditional correlation tests.

Linear regression offers a robust alternative as it does not necessitate normality for the dependent variable. By regressing GEBVs over Cortisol, we can model the relationship between these variables. Our aim is to estimate the regression coefficient, which serves as our correlation estimate.

Due to the violation of normality assumptions for the dependent variable (Cortisol), traditional correlation tests may not provide reliable results, particularly in assessing the significance of the correlation. Therefore, alternative approaches, such as linear regression, are preferred as they do not require the same assumptions about the distribution of the dependent variable. By using linear regression, we can still assess the relationship between Cortisol and GEBVs while accommodating the non-normality of Cortisol phenotypes.

The regression model can be represented as follows: \[ y = \beta_0 + \beta_1 \times GEBV_{\text{Milk}} + \epsilon \]

Where:

\(y\) represents Cortisol phenotypes.
\(GEBV_{\text{Milk}}\) denotes the GEBV for Milk Yield.
\(\beta_0\) and \(\beta_1\) are the intercept and regression coefficient, respectively.
\(\epsilon\) represents the error term capturing unexplained variability.

This approach enables us to quantify the relationship between Cortisol and GEBVs, addressing the non-normality of Cortisol phenotypes while allowing for formal hypothesis testing of the correlation’s significance.

10.1 Data preparation

The first data I received from Lucas had only 135 animals out of 260 with values the other 125 had only NA I shown this to Lucas Lucas wrote to Alisson Lucas sent me the missing animals I merged this two files

rm(list = ls())

# Load the necessary library
library(dplyr)
library(tidyverse)

cortisol_260 <- read.csv("/home/bambrozi/2_CORTISOL/Data/data_clean.csv")

#This is the first dataframe with information for 135 animals and 125 NA
GEBVs1 <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/ebvs_elora.csv")
#This is the second file with information for the 125 NA animals
GEBVs2 <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/elora_missing_females_2404_06_11_2024.csv")
#This are de columns we can use because we know the meaning of the acronyms
GEBVs_to_use <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/ebv_names_lucas_06102024_BAG.csv")


sum(is.na(GEBVs1$MILK))
GEBVs1<- GEBVs1[which(is.na(GEBVs1[,"DHI_BARN_NAME"]) == F),]

sum(!is.na(GEBVs2$MILK))
GEBVs2<- GEBVs2[which(is.na(GEBVs2[,"DHI_BARN_NAME"]) == F),]

print(GEBVs1$DHI_BARN_NAME)
print(GEBVs2$DHI_BARN_NAME)

# Making the two dataframes with the same columns
# Remove elora_id and international_id from GEBVs1
GEBVs1 <- GEBVs1 %>% select(-elora_id, -international_id)

# Remove ANIMAL_ID from GEBVs2
GEBVs2 <- GEBVs2 %>% select(-ANIMAL_ID)

# Check if the two dataframes have the same columns
have_same_columns <- all(names(GEBVs1) == names(GEBVs2))

if (have_same_columns) {
  print("The dataframes have the same columns.")
} else {
  print("The dataframes do not have the same columns.")
}


# Check if the column names are in the same order
same_order <- identical(names(GEBVs1), names(GEBVs2))

if (same_order) {
  print("The columns are in the same order.")
} else {
  print("The columns are not in the same order.")
}

GEBVs_combined <- rbind(GEBVs1, GEBVs2)

# Sort the columns
sorted_cortisol_260 <- sort(cortisol_260$ID)
sorted_GEBVs_combined <- sort(GEBVs_combined$DHI_BARN_NAME)

# Check if the sorted columns have the same values
identical(sorted_cortisol_260, sorted_GEBVs_combined)

# Create a duplicate of the column 'DHI_BARN_NAME' and name it 'elora_id'
GEBVs_combined$elora_id <- GEBVs_combined$DHI_BARN_NAME

# Assuming GEBVs_combined is your data frame
GEBVs_combined <- GEBVs_combined %>%
  select(elora_id, DHI_BARN_NAME, everything())

write.csv(GEBVs_combined, "/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/ebvs_elora_complete.csv")

# Merging the dataframe with Cortisol values, with the dataframe with GEBVs values
Merg_Cort_GEBVs <- merge(cortisol_260, GEBVs_combined, by.x = "ID", by.y = "elora_id")

write.csv(Merg_Cort_GEBVs, "/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/Merged_Cortisol_GEBVs.csv")

#Opening the file with the GEBVs columns to use
Columns_to_use <- readLines("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/traits_to_use.txt")

colnames(Merg_Cort_GEBVs)[405] <- "IDD"

data <- select(Merg_Cort_GEBVs, ID, T4Cortisol, BIRTH_YEAR, all_of(Columns_to_use))

# The data below has the the 55 GEBVs + Cortisol data + Birth Year
write.csv(data, "/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/data_GEBVs_Cortisol_select_traits.csv")

samp_date2 <- read.csv("/home/bambrozi/2_CORTISOL/Data/Elora animal_ids_kl_sampling_date.csv")

# Convert Sampling_date to Date using as.Date
samp_date$Sampling_date <- as.Date(samp_date$Sampling_date, format = "%m/%d/%Y")

table(samp_date$Sampling_date)

samp_date <- select(samp_date, Elora_id, Sampling_date)

# Check if data$ID and samp_dates$elora_id are identical in values and order
identical(data$ID, samp_date$Elora_id)

data_final <- merge(data, samp_date, by.x="ID", by.y="Elora_id")

data_final <- data_final %>%
  select(ID, T4Cortisol, BIRTH_YEAR, Sampling_date, everything())

# The data below has the the 55 GEBVs + Cortisol data + Birth Year + Sampling data
write.csv(data_final, "/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/data_GEBVs_Cortisol_select_traits2.csv")

ps. I double checked by hand the select and merge process against the original tables received and is everything ok.

10.2 Correlations - Linear Regression

We tested the minimum model presented above, and adding only Birth Year, but adding Birth Year and Sampling Date we got the best results.

10.2.1 Adding BIRTH_YEAR and SAMPLING DATE

The regression model added the BY and SAMPLING DATE is shown bellow:

\[ y = \beta_0 + \beta_1 \times GEBV_{\text{Trait}} + BIRTH\_YEAR + SAMPLING\_DATE + \epsilon \]

Where:

\(y\) represents Cortisol phenotypes.
\(GEBV_{\text{Trait}}\) denotes the GEBV for the specific trait (e.g., Milk Yield).
\(BIRTH\_YEAR\) is the birth year of the subjects, included as a factor.
\(SAMPLING\_DATE\) is the cortisol sampling date for the subjects, included as a factor.
\(\beta_0\) and \(\beta_1\) are the intercept and regression coefficient, respectively.
\(\epsilon\) represents the error term capturing unexplained variability.

The SAMPLING_DATE variable is also converted to a factor to account for the categorical nature of sampling date.

# Convert BIRTH_YEAR to a factor and rename
data_final$BIRTH_YEAR <- as.factor(data_final$BIRTH_YEAR)

# Convert Sampling_data to a factor and rename
data_final$Sampling_date <- as.factor(data_final$Sampling_date)

# Initialize a list to store the results
results_list <- list()

# Loop through columns 3 to ncol(data) for the GEBVs
for (i in 5:ncol(data_final)) {
  trait_name <- colnames(data_final)[i]
  
  # Fit the linear regression model with BIRTH_YEAR as an additional predictor
  model <- lm(data_final[[2]] ~ data_final[[i]] + data_final$BIRTH_YEAR + data_final$Sampling_date , data = data_final)
  
  # Summarize the model
  model_summary <- summary(model)
  
  # Extract the desired statistics
  multiple_r_squared <- model_summary$r.squared
  adjusted_r_squared <- model_summary$adj.r.squared
  f_statistic <- model_summary$fstatistic[1] # F-statistic value
  f_num_df <- model_summary$fstatistic[2] # Numerator degrees of freedom
  f_den_df <- model_summary$fstatistic[3] # Denominator degrees of freedom
  p_value <- pf(f_statistic, f_num_df, f_den_df, lower.tail = FALSE) # P-value
  
  # Extract the coefficient and its p-value for the trait
  coef_summary <- coef(model_summary)
  trait_coef <- coef_summary[2, "Estimate"]  # Assumes the trait is the second predictor
  trait_p_value <- coef_summary[2, "Pr(>|t|)"]
  
  # Combine the statistics into a data frame
  result <- data.frame(
    Trait = trait_name,
    Multiple_R_Squared = multiple_r_squared,
    Adjusted_R_Squared = adjusted_r_squared,
    F_Statistic = f_statistic,
    P_Value = p_value,
    Coefficient = trait_coef,
    Coefficient_P_Value = trait_p_value
  )
  
  # Append the result to the results list
  results_list[[i - 2]] <- result
}

# Combine all results into a single data frame
results_df <- do.call(rbind, results_list)

# Save the results to a CSV file
write.csv(results_df, file = "/home/bambrozi/2_CORTISOL/Correlation/Results/add_BY_SAMP/regression_summary_all_traits_BY_SampDt.csv", row.names = FALSE)

Summary statistics for all Traits’ GEBVs adding BIRTY_YEAR and SAMPLING_DATE

Trait	Multiple_R_Squared	Adjusted_R_Squared	F_Statistic	Coefficient	Coefficient_P_Value
CO	0.3913617	0.3257757	5.967151	-11.1849879	0.0099861
BMR	0.3899307	0.3241904	5.931386	14.4612444	0.0135665
LP	0.3858330	0.3196512	5.829896	12.2619950	0.0330355
MILK	0.3850052	0.3187343	5.809559	0.0671160	0.0396652
PROT	0.3841834	0.3178238	5.789422	2.2166395	0.0476301
UT	0.3822859	0.3157218	5.743130	-12.1238483	0.0731558
CK	0.3801906	0.3134008	5.692345	12.7333709	0.1192726
HHE	0.3795579	0.3126999	5.677076	7.4003505	0.1388525
MSP	0.3793006	0.3124149	5.670876	-7.2388952	0.1478152
DA	0.3791391	0.3122360	5.666989	10.8037510	0.1537699
TU	0.3785722	0.3116080	5.653351	-5.2064639	0.1769405
MSL	0.3777098	0.3106526	5.632656	-8.6230634	0.2203509
BQ	0.3775224	0.3104450	5.628166	-7.7284891	0.2313766
IH	0.3772456	0.3101385	5.621542	-4.7354916	0.2488963
ST	0.3767980	0.3096426	5.610839	-9.9721644	0.2808121
LOC	0.3766890	0.3095218	5.608233	-7.2367609	0.2893509
FAT	0.3765584	0.3093772	5.605116	0.8520581	0.3000062
UD	0.3763515	0.3091480	5.600177	-9.3297651	0.3179533
CA	0.3757417	0.3084725	5.585641	6.0756898	0.3798799
MS	0.3755038	0.3082090	5.579979	-6.0375749	0.4085912
SCK	0.3754363	0.3081342	5.578372	-4.4161473	0.4173165
SCS	0.3754033	0.3080976	5.577587	3.9080681	0.4216769
IDD	0.3752677	0.3079474	5.574362	3.7029601	0.4403519
FOOT	0.3751830	0.3078536	5.572349	17.3309360	0.4526548
TL	0.3750803	0.3077398	5.569908	-6.6585204	0.4683140
MET	0.3749055	0.3075462	5.565755	-3.4014191	0.4970656
CTFS	0.3747600	0.3073850	5.562300	4.0557716	0.5233387
CONF	0.3746137	0.3072229	5.558828	-4.7896643	0.5523352
FE	0.3745078	0.3071056	5.556316	-2.9375586	0.5752627
HL	0.3743988	0.3069849	5.553731	3.4274416	0.6009102
FA	0.3742870	0.3068610	5.551080	-3.2821641	0.6298652
DD	0.3742836	0.3068573	5.551001	2.5402141	0.6307730
MOB	0.3742556	0.3068263	5.550337	3.0592146	0.6385442
FTP	0.3742359	0.3068045	5.549870	4.8583743	0.6441419
BCS	0.3741681	0.3067293	5.548263	2.6692352	0.6643615
HH	0.3740753	0.3066266	5.546066	2.0230249	0.6947785
DF	0.3740392	0.3065865	5.545209	2.3681806	0.7076996
FL	0.3739824	0.3065237	5.543865	-2.1711035	0.7294613
UF	0.3739605	0.3064994	5.543347	2.8880683	0.7384413
MDR	0.3738855	0.3064163	5.541571	1.8495373	0.7722558
WL	0.3738548	0.3063822	5.540843	1.2643028	0.7878801
AFS	0.3738293	0.3063540	5.540239	1.5814380	0.8018670
RUM	0.3738023	0.3063241	5.539601	-1.2574561	0.8179113
SH	0.3738015	0.3063233	5.539583	1.0342843	0.8184040
BD	0.3736958	0.3062061	5.537080	0.7613199	0.9071017
MR	0.3736957	0.3062060	5.537078	0.6291752	0.9071898
SU	0.3736871	0.3061965	5.536876	0.4745538	0.9186634
FEED	0.3736818	0.3061907	5.536751	0.3944746	0.9266753
DHL	0.3736774	0.3061858	5.536647	-0.5919776	0.9340753
DO	0.3736757	0.3061838	5.536604	0.5220723	0.9373261
DS	0.3736695	0.3061769	5.536458	0.3979132	0.9502668
CW	0.3736676	0.3061749	5.536414	0.3562368	0.9547808
ME	0.3736658	0.3061729	5.536370	-0.2401976	0.9599012
MT	0.3736595	0.3061659	5.536223	-0.0976386	0.9882642
DCA	0.3736593	0.3061657	5.536217	0.0802555	0.9906432

Fitting Birth_Year and Sampling_date to the model these are the traits with significant correlation (<0.15):

CO = Cystic ovaries
BMR = Body Maintenance Requirements
LP = Lactation persistency
MILK = Milk yield
PROT = Protein yield
UT = Udder Texture
CK = Clinical Ketosis
HHE = Heel Horn Erosion
MSP = Milking Speed

11 Heritability estimation - BLUPF90

11.1 Files preparation

Preparing files to run Variance components estimation using REML with AI (Average Information) algorithm.

First you need to create a directory in your home directory, prepare and save the following files in:

Phenotype and Fixed effects file
Pedigree file
Genotype file
BlupF90+ executable file
RenumF90 executable file
Parameter file

11.2 Running renumF90 and BlupF90+

Go to the server you wanna run this analysis, for instance, grand
Now go to the directory you have created to run this analysis where that set of files are placed.

ssh grand

Make the renumF90 and BlupF90+ files executables

chmod +x renumf90
chmod +x blupf90+

Run renumF90

./renumf90

When you run the code above, it will as you the name of your parameter card.

renumF90 will generate a new parameter card called renf90.par

Run blupf90+

./blupf90+

blupf90+ will ask you for parameter’s card name, now you should provide with the new one renf90.par

blupF90+ will generate the blupf90.log file with the results.

My Image

Now you should update you renf90.par file with these informations from the .log file

Copy Residual Variance from blupf90.log and will paste on renf90.par RANDOM_RESIDUAL_VALUES Copy Genetic variance for effect x from blupf90.log and will paste on renf90.par (CO) VARIANCE

2nd blupf90+ run

./blupf90+

blupf90+ will ask you for parameter’s card name, now you should provide with the UPDATED renf90.par

11.3 Running renumF90 and BlupF90+ adding GENOTYPES

The previous analysis considered only the pedigree, but now we can insert the genotype information. To perform this you need a new diretory called Blup_Genomic inside your previously created directory.

Now you need add the reference for your genotype file in your previous parameter file renum.par and save in this new sub-directory.

The highlighted text show the added part.

My Image

Go to the sub-diretory

Note that as you are in the subdirectory, but your phenotype and fixed effect, pedigree and genotype files are still in the previous directory you need to add the highlighted part to inform the correct location

My Image

To run the renumf90 and blupf90+ you also need to add ../ to correct specify the location. Run renumF90

./../renumf90

Run blupf90+

./../blupf90+

The steps for run, update parameter card, re-run are the same.

11.4 Results

We have 2 different output files

Variance components: blupf90.log

My Image

In this file we can find the heritabilit (SD) and for instance the convergence (similarity)

Solutions: solutions.orig
In this file we will find the solutions (results) for each effect

In our example:

EFFECT 1: Birth Year, has 4 levels (2018, 2019, 2020 and 2021), and the solution that for this fixed effect is how much each level add.
EFFECT 2: Sampling date, has 23 levels, and the solutions
EFFECT 3: Animal random effect, has one for each animal and it is the EBV or GEBV.

Read_me

Galindo, B. A

2024-05-22

1 Raw data

2 Continuous Phenotype

2.1 Categorical Phenotype

2.2 Removing “outliers”

3 GENOTYPES

3.1 Transforming the Genotypes in 012 format to plink format

4 GWAS

4.1 Checking with Phenotyped Animals also have Genotype

4.2 Generating a Phenotype file

4.3 Adjusting the SNP_map to .map

4.4 Generating the bfiles

5 Quality Control

6 KING

7 PCA

7.1 PCA Plot

8 GWAS on GCTA

9 GWAS - EXTREME PHENO - WITH BY and SD

9.1 Data preparation

9.2 GWAS on GCTA

9.3 BLUPF90+ GWAS

9.3.1 Files preparation

9.3.1.1 Phenotype and Fixed effects file

9.3.1.2 Pedigree file

9.3.1.3 Genotype file

9.3.2 Download the executable files

9.3.3 SNP MAP

9.3.4 Parameter file for Quality Control

9.3.5 Prediction of SNP marker effects based on the GEBV

9.3.6 GWAS for High and Low cortisol animals

9.3.6.1 Updating the files

9.3.6.1.1 Phenotype and Fixed Effects files

9.3.6.1.2 Genotype files

9.3.6.2 Running renum_QC.par

9.3.6.3 Running renf90_DataClean.par for Quality Control

9.3.6.4 Running renf90_ssGWAS1_Ginv.par for Ginv estimation

9.3.6.5 Running renf90_ssGWAS2_SNPeff.par for GWAS

9.3.6.6 Manhattan Plots for BLUPF90 GWAS

9.3.6.6.1 Genome Independent Segment

9.3.6.7 Get rsID

9.3.6.7.1 Step 01 = Bring the SNP name to GWAS output

9.3.6.7.2 Step 02 = Bring the rsID to the file with SNP name.

9.3.7 BLUPF90+ GALLO

9.3.7.1 QTL enrichment on GALLO

9.3.7.2 BLUPF90+ GPROFILER ON-LINE

9.3.7.3 BLUPF90+ VEP

9.4 BLUPF90+ WINDOWS

9.4.1 Running renf90_ssGWAS2_SNPeff_w.par for GWAS (WINDOWS)

9.4.1.1 Manhattan Plots for BLUPF90 Windows ssGWAS

9.4.2 0.5% Windows - BLUPF90+ GALLO

9.4.2.1 Windows - QTL enrichment on GALLO

9.4.3 0.1% Windows - BLUPF90+ GALLO

9.4.3.1 Windows - QTL enrichment on GALLO

9.5 Comparison among ssGWAS for independent SNPs against Window 0.5% and window 0.1%

9.5.1 SNPs

9.5.2 Genes

9.5.3 QTLs

9.6 Allele frequency for GWAS output

9.6.1 “Regular” ssGWAS

9.6.2 Windows ssGWAS

9.6.2.1 0.5%

9.6.2.2 0.1%

10 Genetic Correlation

10.1 Data preparation

10.2 Correlations - Linear Regression

10.2.1 Adding BIRTH_YEAR and SAMPLING DATE

11 Heritability estimation - BLUPF90

11.1 Files preparation

11.1.1 Phenotype and Fixed effects file

11.1.2 Pedigree file

11.1.3 Genotype file

11.1.4 Download the executable files

11.1.5 Parameter file

11.2 Running renumF90 and BlupF90+

11.3 Running renumF90 and BlupF90+ adding GENOTYPES

11.4 Results